An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8

Abstract

Aiming at the problem that insulator image backgrounds are complex and fault types are diverse, which makes it difficult for existing deep learning algorithms to achieve accurate insulator fault diagnosis, an insulator fault diagnosis method based on multi-mechanism optimization YOLOv8-DCP is proposed. Firstly, a feature extraction and fusion module, named CW-DRB, was designed. This module enhances the C2f structure of YOLOv8 by incorporating the dilation-wise residual module and the dilated re-param module. The introduction of this module improves YOLOv8’s capability for multi-scale feature extraction and multi-level feature fusion. Secondly, the CARAFE module, which is feature content-aware, was introduced to replace the up-sampling layer in YOLOv8n, thereby enhancing the model’s feature map reconstruction ability. Finally, an additional small-object detection layer was added to improve the detection accuracy of small defects. Simulation results indicate that YOLOv8-DCP achieves an accuracy of 97.7% and an [email protected] of 93.9%. Compared to YOLOv5, YOLOv7, and YOLOv8n, the accuracy improved by 1.5%, 4.3%, and 4.8%, while the [email protected] increased by 3.0%, 4.3%, and 3.1%. This results in a significant enhancement in the accuracy of insulator fault diagnosis.

1. Introduction

The power grid, as a critical infrastructure for electrical transmission, plays an indispensable role in both civilian and industrial sectors [1]. Insulators, as key components in transmission lines, primarily serve the functions of supporting conductors and providing electrical isolation [2]. However, insulators exposed to harsh environmental conditions over extended periods are inevitably subject to failures such as self-explosion, broke, and flashover. These failures not only degrade the performance of the insulators and increase the risk of transmission equipment malfunction but also have the potential to lead to safety incidents. Therefore, regular inspection and maintenance of insulators are crucial for ensuring the stability and safety of the power transmission process. Currently, traditional methods for diagnosing insulator faults largely rely on manual inspections, which are not only inefficient but also characterized by high missed detection rates and significant safety risks [3]. Consequently, there is a pressing need for a safe and efficient method for insulator fault diagnosis.

In recent years, with the rapid development of deep learning, significant progress has been made in the field of object detection. Object detection algorithms can generally be classified into two categories: two-stage algorithms and single-stage algorithms. Classic two-stage algorithms include R-CNN [4], Fast R-CNN [5], and Faster R-CNN [6,7], while classic single-stage algorithms include the YOLO [8,9,10,11,12] and SSD [13]. Among these, the YOLO series of algorithms, known for their rapid detection speed and broad applicability, have been extensively improved and utilized by many researchers for various detection tasks. For example, Wang et al. [14] proposed the BL-YOLO algorithm, which combines attention mechanisms and the BiFPN structure to detect road defects, achieving an average recognition accuracy of 90.7%. Mihajlo et al. [15] successfully employed YOLOv8 to differentiate between drone control signals and other signals, attaining an average recognition accuracy of 90%. The YOLO series of algorithms has also demonstrated significant potential in the field of insulator fault diagnosis. For instance, Souza [16] recently introduced a Hybrid-YOLO algorithm that combines ResNet-18 with YOLO, achieving high-precision insulator fault diagnosis. This approach outperforms architectures such as VGG16, ResNet-50, and DenseNet-152, highlighting the strong performance of YOLO algorithms in the domain of insulator faults. Wei et al. [17] improved YOLOv5 by incorporating the Mish activation function and the CBAM attention mechanism, enabling effective insulator fault diagnosis under varying weather conditions and significantly enhancing diagnostic accuracy. Zhou et al. [18] designed an attention mechanism that integrates channel and spatial information to improve the YOLOv5 algorithm. This enhancement enables the model to rapidly locate the area of the insulator and facilitates quick diagnosis of insulator faults.

Although the aforementioned studies have contributed to insulator fault diagnosis, most of them are limited in their ability to diagnose a single type of insulator fault and still have room for improvement in detection accuracy. Additionally, the presence of diverse backgrounds and varying fault scales in insulator images poses challenges for existing models, which struggle to effectively extract and fuse multi-scale target feature information. Lastly, the challenge of diagnosing small faults with varying scales remains significant for these models. With the continuous iterations of YOLO series algorithms, YOLOv8 has demonstrated remarkable potential. For instance, Hussain [19] compared the detection performance of YOLOv1 through YOLOv8, revealing the robust capabilities of YOLOv8 in the field of object detection. Satyajit et al. [20] integrated YOLOv8 with a drone-based image acquisition system, enabling rapid detection of insulator faults, which highlights YOLOv8’s superior detection speed and accuracy. This paper selects the YOLOv8 algorithm as the baseline model for optimization to achieve high-precision diagnosis of insulator faults such as damage, self-destruction, and flashover.

To address the above problems in insulator fault diagnosis, this paper proposes an insulator fault diagnosis algorithm YOLOv8-DCP with enhanced feature extraction and feature fusion. In this study, the targets to be detected are classified into four categories: insulators and three types of faults. The category “insulator” refers to the entire insulator string, regardless of whether it has defects. The three types of faults are self-explosion, breakage, and flashover. The proposed algorithm allows for direct detection of insulator images to achieve fault diagnosis and localization. Comparative analysis with various mainstream algorithms demonstrates that the YOLOv8-DCP algorithm can effectively identify multiple types of insulator faults and accurately localize them.

The main contributions of this paper are as follows:

(1)

This paper proposes an algorithm based on multi-mechanism optimization of YOLOv8 to achieve high-precision multi-class fault diagnosis for insulators. The C2f module of YOLOv8 is improved by using dilatable residual module and dilated re-param block and named as CW-DRB feature extraction and feature fusion module to enhance the model’s multi-scale feature extraction and fusion capabilities.

(2)

The feature content-aware module CARAFE is introduced to replace the original up-sampling layer in YOLOv8, enhancing the model’s feature reconstruction capability and preserving key feature information in the feature maps. Additionally, a small-object detection layer is added to the backbone network to improve the detection accuracy of small faults.

(3)

Image processing methods are used to expand the image samples in the data set and establish a data set containing multiple types of faults. By comparing the performance of the algorithm proposed in this paper with other algorithms, it is verified that the algorithm can maintain a high detection accuracy when there are multiple faults in the image and the fault scales are different. The performance is better than other algorithms, and the diagnosis accuracy of multiple types of insulator faults is effectively improved.

The rest of this paper is organized as follows: Section 2: Model and Methods. Section 3: Experimental and Analysis. Section 4: Discussion. Section 5: Conclusion and Future Research Directions.

2. Model and Methods

2.1. YOLOv8 Network Architecture

The YOLOv8 (You Only Look Once version 8) [21] algorithm, introduced by Ultralytics in 2023, is a new version in the YOLO series. Compared to its predecessors, YOLOv8 offers improved detection speed and accuracy. The network architecture is illustrated in Figure 1. The network structure of the YOLOv8 algorithm is divided into four parts: the Input Layer, Backbone Network, Neck, and Head. The Input Layer is responsible for adjusting the size of the input images. The Backbone layer employs the C2f structure based on the CSPNet framework, which maps channel features to focal features, facilitating the extraction and prioritization of key information. Its primary function is to extract feature information from the images. The Neck layer integrates the PANet structure to achieve seamless information transfer and aggregation through multiple pathways, effectively combining global and local feature insights. Its primary function is to fuse the feature information. The Head layer replaces the Coupled-Head with the current mainstream Decoupled-Head structure to predict features, thereby obtaining object location and classification information.

2.2. Improved YOLOv8 Model

At present, there is still potential to improve the detection accuracy of algorithms in the field of insulator fault diagnosis. At the same time, there are also problems such as the presence of multiple multi-scale targets in insulator images and the different scales of similar faults in the images, which brings problems to model training. Therefore, the purpose of this study is to improve the original YOLOv8 algorithm to realize multi-scale fault recognition of insulator images and improve the accuracy of insulator fault diagnosis. This paper proposes an insulator fault diagnosis algorithm based on the improved YOLOv8 model. The new algorithm is named YOLOv8-DCP, and the overall structure of the improved algorithm is shown in Figure 2.

In order to solve the current problems in insulator fault diagnosis, this paper adopts three methods to improve the performance of the model. At the same time, in order to more directly show the correspondence between these improvement methods and the model improvement ability, Figure 3 is drawn. This figure shows the problems that need to be studied. For these problems, three improvement modules are proposed to improve YOLOv8 in a targeted manner and improve the accuracy of insulator fault diagnosis.

2.3. C2f-DWR Feature Extraction Module

In YOLOv8, the C2f structure can be viewed as a multi-layer residual network consisting of two convolutional layers and several BottleNeck modules, designed to pass features after extraction and fusion. Since the original C2f structure cannot effectively extract features of multi-scale targets, a feature extraction module based on dilated residuals, C2f-DWR, is proposed to enhance the model’s ability to capture detailed features from high-level feature maps. This is achieved by replacing the BottleNeck modules in C2f with DWR (dilation-wise residual) [22]. The original C2f and C2f-DWR structures, along with the principles of DWR, are illustrated in Figure 4.

The primary function of the DWR module is to perform multiple extractions of feature information from the feature maps, ensuring the completeness of the extracted feature information by the model. The DWR module is designed using a residual approach, dividing the feature extraction process into two steps. The first step involves generating different residual features from the input features. This step is accomplished through a combination of a 3 × 3 convolution, batch normalization (BN), and ReLU activation function. The operation can be represented by Equation (1) [22].

$$C_1\left(x\right)=SiLU\left(BN\left(Conv\left(x\right)\right)\right)$$

(1)

In the equation, SiLU represents the activation function, BN denotes batch normalization, and Conv refers to a 3 × 3 convolution. The second step involves using dilated depth-wise convolutions with multiple dilation rates to perform morphological filtering on residuals from different-sized regions. This operation ensures that only a single desired receptive field is applied within the channels. The purpose of the dilated depth-wise convolutions is to capture as much complex semantic information as possible; however, through the DWR module, this process is adapted to perform morphological filtering to achieve the desired receptive field for each simplified feature map. This operation is expressed by Equation (2) [22].

$$\left\{\begin{array}{l}{C}_2\left(x\right)=D_{d}Donv\left(C_1\left(x\right)\right)\\ DWR\left(x\right)=PConv\left(BN\left(\Gamma d\left\{C_2\left(x,d\right)\right\}\right)\right)+x\end{array}\right.$$

(2)

where $C_1$ represents the first step of the DWR operation, $C_2$ represents the second step of the DWR operation,$D_{d}Donv$ is a 3 × 3 convolution with dilation d, $PConv$ is a point-by-point convolution, and $\Gamma d$ is a cascade operation. After completing the above two steps, the DWR module uses pointwise convolution to merge all residuals, resulting in a more comprehensive and semantically rich feature representation.

2.4. CW-DRB Multi-Level Feature Fusion Module

To enhance the model’s ability to capture contextual information from feature maps and improve the fusion of features at different levels, the DRB (dilated re-param block) [23] module is introduced to improve the DWR module in the C2f-DWR structure, and the final module is named CW-DRB. The DRB module structure and CW-DRB structure are shown in Figure 5.

The primary function of the DRB module is to further extract and integrate feature information at multiple levels, ensuring that the output feature maps contain rich semantic information. Its principle involves performing multiple feature extractions and transformations on the input feature maps through convolution, enabling each convolutional layer to learn feature representations from different levels. Subsequently, multiple fusion layers combine and output feature information from various layers. Therefore, the CW-DRB module, enhanced with DRB, significantly improves the model’s feature fusion capabilities, strengthens feature representation, and enriches the diversity of feature maps by integrating features from different levels as comprehensively as possible.

2.5. CARAFE Lightweight Operator

The CARAFE (Content-Aware ReAssembly of Features) module [24] addresses the limitations of traditional feature up-sampling methods in retaining details and reconstructing semantic information. Its primary function is to perform up-sampling on the input feature maps while ensuring the integrity of semantic information. Its structure is illustrated in Figure 6.

The CARAFE module has three main advantages: (1) It has a large receptive field, allowing it to aggregate contextual information. (2) It is lightweight, adding minimal computational overhead, and can be flexibly integrated into network architectures. (3) It has content-aware processing capabilities, enabling CARAFE to dynamically generate adaptive kernels to process features from different samples. As shown in Figure 5, the up-sampling principle of CARAFE consists of two steps: the first step involves predicting specific adaptive kernels based on the content at each target location, and the second step involves feature reconstruction through kernel reassembly to obtain the up-sampled feature map. For a feature input $H\times W\times C$ of a given size and an up-sampling ratio $\sigma \in Z$, a feature map of size $\sigma H\times \sigma W\times C$ is generated by the kernel prediction module. The coordinates of the target point in the output feature map correspond to the original coordinates, and their coordinate relationship is shown in Equation (3) [24].

$$\left\{\begin{array}{l}l=\left(i,j\right),l^{\prime }=\left(i^{\prime },j^{\prime }\right)\\ i={\displaystyle \frac{i^{\prime }}{\sigma }},j={\displaystyle \frac{j^{\prime }}{\sigma }}\end{array}\right.$$

(3)

In the equation, $\sigma$ represents the down-sampling ratio, $l$ denotes the coordinates before up-sampling, $l^{\prime }$ denotes the coordinates after up-sampling, $\left(i,j\right)$ represents the original coordinates before sampling, and $\left(i^{\prime },j^{\prime }\right)$ denotes the coordinates after sampling.

Taking the target point as an example, the kernel prediction module works by first adjusting the number of channels by performing a 1 × 1 convolution on the input features. Then, a recombination kernel is generated by content encoding, and a recombination kernel of size $k_{up}\times k_{up}$ is generated for the point after normalizing the kernel using the softmax function. Finally, feature recombination is performed for all points in the region $N\left(Xl,k_{up}\right)$ centered on the point. The output result is shown in Equation (4) [24].

$$X_{l^{\prime }}^{\prime }={\displaystyle \sum _{n=-r}^r{\displaystyle \sum _{n=-r}^r{W}_{l^{\prime }\left(n,m\right)}·X_{\left(i+n,j+m\right)}}};r={\displaystyle \frac{k_{up}}{2}}$$

(4)

where $W_{l^{\prime }\left(n,m\right)}$ denotes the reorganization kernel of the point, $X_{l^{\prime }}^{\prime }$ is the post-output feature map, and $X_{\left(i+n,j+m\right)}$ is the pixel point of the neighboring region. $k_{up}$ is the size of the reorganized kernel. Therefore, to address challenges such as narrow target perception range within images, insufficient fault feature details, and suboptimal feature information utilization, incorporating CARAFE to improve YOLOv8 enhances the model’s ability to better integrate semantic information from different regions and strengthens the model’s capability to extract detailed fault features.

2.6. Small Target Fault Detection Layer

The addition of a small fault detection layer aims to address the challenge of extracting features for faults that are small in scale and have less pronounced details. This is because, for YOLOv8, learning the characteristics of smaller faults within deep feature maps is quite difficult, especially when objects in insulator images are subject to mutual occlusion.

As shown in the original YOLOv8 network (Figure 1), the input image is resized to 640 × 640 × 3 at the input stage. In the Neck network, after various convolutional and feature fusion operations, three feature maps of different sizes are output: P3, P4, and P5. The P3 layer has a feature map size of 80 × 80 × 64 and is used for detecting larger objects within the image. The P4 layer has a feature map size of 40 × 40 × 128 and is used for detecting medium-sized objects. The P5 layer has a feature map size of 20 × 20 × 256 and is used for detecting smaller objects within the image.

The addition of a small target fault detection layer, P2, with a size of 160 × 160 × 32, connected to the Head layer, addresses the issue of extracting small fault features. Although shallow networks have a limited receptive field and weaker semantic information, they are very effective at representing fine details of the target. Additionally, small-sized feature maps often suffer from information loss during feature propagation, which can result in less noticeable small fault features in deeper networks. Therefore, incorporating the P2 layer enhances the model’s ability to learn small fault features by allowing it to extract more detailed information from the shallow layers. This improves the model’s precision in detecting small faults in insulators. The comparison between the integrated and original paths is shown in Figure 7.

3. Experimental and Analysis

3.1. Materials

Due to the lack of authoritative public datasets for different types of insulator faults, this study created its own dataset by collecting insulator images from the internet and using datasets provided by collaborating institutions. The dataset includes images of normal insulators, infrared insulators, as well as images depicting damage, self-explosion, and flashover faults. However, since infrared insulators are a type of insulator, they are categorized together with the normal insulators. Image annotation was performed using the LabelImg v1.8.1. To enhance the dataset, various image augmentation methods were employed, resulting in a final dataset containing 3316 images with diverse fault types. Additionally, since the same image may contain different types of faults and categories, the number of labels for all images in the augmented dataset was statistically analyzed. The results are shown in

Table 1. The number of labels for each type of target in the dataset.

Label Name	Number of Labels
Insulator	6106
Defect	369
Broke	892
Flash-dirty	3576

. Representative images of these four categories are illustrated in Figure 8.

The dataset employed in this research comprises four image categories: standard insulators, breakage, self-detonation, and flashover. Given that the primary dataset does not sufficiently fulfill the criteria for experimental generalization and universality, the dataset has been expanded through the application of diverse image processing techniques. There are five common methods of image expansion, as follows [25]:

(1)

Adjust contrast: By adjusting the pixel values of different areas of the image, the purpose of enhancing the differences between different areas of the image is achieved;

(2)

Gaussian blur: reduces the feature details of the target and makes the image blurry;

(3)

Random occlusion: introduce a random occlusion matrix into the image to cover the original feature information of the image;

(4)

Add noise: set the pixel values of pixels at random positions in the image to 255 or 0;

(5)

Proportional scaling: scale the image proportionally without changing the proportion of the target in the image and fill the scaling edges;

The above five methods were used to process the images, and a comparison chart of the results from each image processing method is shown in Figure 9. This paper achieves the goal of expanding the dataset without distorting the images by randomly applying multiple image processing techniques simultaneously to the original images.

3.2. Experimental Environment and Parameter Settings

In this paper, the environment is configured as follows: the operating system is Windows 10, the CPU model is Intel(R) Core(TM) i7-13700K (Intel, Santa Clara, CA, USA), the GPU model is NVIDIA GeForce RTX 3060 (NVIDIA, Santa Clara, CA, USA), and the version of Python used is 3.11, using pytorch 2.2.0 network framework. Additionally, the dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The specific model training parameters are shown in

Table 2. Model training parameter setting table.

Model Parameter	Parameter Settings
Image size	640 × 640 × 3
Epochs	300
Batch size	16
Learning rate	0.01
Optimizer	SGD
Device	0

.

In Table 2, Image size denotes the dimensions of the input images, Epochs indicates the number of training iterations, and Batch size refers to the number of samples used for training per iteration. Given resource constraints, this paper selects a batch size of 16. The learning rate determines the magnitude of weight adjustments; an appropriate learning rate enables the model to converge quickly, while a rate that is too high or too low can result in unsuccessful training. This paper selects a default value of 0.01. Optimizer determines the strategy for updating model parameters. The chosen Stochastic Gradient Descent (SGD) is a widely used algorithm for optimizing deep learning models, which utilizes only a small subset of data per iteration, making SGD faster and more efficient compared to gradient descent that uses the entire dataset. Device specifies the training hardware setup, with a setting of 0 indicating the use of a GPU for model training.

The model performance evaluation metrics used are Recall (R), Precision (P), Average Precision (AP), and mean Average Precision (mAP). The calculation equation is (5) [26].

$$\left\{\begin{array}{l}P={\displaystyle \frac{TP}{TP+FP}}\\ R={\displaystyle \frac{TP}{TP+FN}}\\ AP={\displaystyle {\int }_0^{1}PR}dr\\ mAP={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=0}^{n}A{P}_i}\end{array}\right.$$

(5)

In the equation, P represents precision, R denotes recall, TP is the number of true positive samples correctly identified, FP is the number of negative samples incorrectly identified as positive, and FN is the number of positive samples incorrectly identified as negative. AP stands for Average Precision, and mAP represents the mean Average Precision across all target classes in the dataset. A higher mAP value indicates better model performance.

3.3. Performance Comparison Experiment

In order to verify the performance of the algorithm in this paper, after setting the experimental parameters, the YOLOv8-DCP algorithm and the YOLOv8n algorithm are used to train on the dataset of this paper at the same time, and the change curves of the performance evaluation indexes of the two models are obtained as Figure 10.

According to Figure 10, in the initial analysis of overall precision (denoted as Precision), it is observed that during the first 55 training rounds, the YOLOv8n algorithm exhibits superior precision compared to the YOLOv8-DCP algorithm. However, in the subsequent 245 training rounds, the YOLOv8-DCP algorithm demonstrates enhanced precision relative to YOLOv8n. Analyzed from the perspective of [email protected], with the increase of training rounds, especially from 100 rounds, the [email protected] value of YOLOv8-DCP gradually increases and the growth curve is smoother, when the training is completed, the [email protected] of YOLOv8n algorithm is 90.8%, and the [email protected] of YOLOv8-DCP algorithm is 93.9%. Overall, the YOLOv8-DCP algorithm has a higher average detection accuracy and better meets the insulator fault diagnosis needs.

To further validate the performance of the CW-DRB module proposed in this paper, various improved C2f modules based on YOLOv8 were selected for training on the dataset used in this study. The results are shown in

Table 3. Comparison results of different C2f improvement modules.

Model	P/%	R/%	[email protected]/%	FPS
YOLOv8	93.8	86.8	90.8	157
YOLOv8-C2f-ODConv [26]	93.9	83.7	90.9	119
YOLOv8-C2f-Faster [27]	93.4	83.9	90.3	166
YOLOv8-C2f-DySnakeConv [28]	93.8	86.3	91.3	125
YOLOv8-CW-DRB	96.1	86.7	92.1	151

.

As shown in Table 3, both C2f-ODConv and C2f-DySnakeConv improve the model’s [email protected], while C2f-Faster leads to a decrease in precision. This is because the Faster structure is designed for model lightweighting, which compromises precision to achieve this goal. In contrast, the CW-DRB module proposed in this paper not only enhances feature extraction capabilities but also provides a more comprehensive fusion of features from different levels. Compared to other improved C2f modules (C2f-ODConv, C2f-Faster, C2f-DySnakeConv), the CW-DRB module achieves improvements of 2.2%, 2.7%, and 2.3% in precision, 3%, 2.8%, and 0.4% in recall, and 1.2%, 1.8%, and 0.8% in mAP, respectively. These comparative results demonstrate the superior performance of the CW-DRB module and validate the effectiveness of the proposed improvements.

3.4. Ablation Experiment

In order to verify the effectiveness of each improvement module proposed in this paper on the performance, a set of ablation experiments are designed for analysis, where “√” indicates that the module is used and “×” indicates that it is not used. YOLOv8n is the baseline model, YOLOv8-C denotes the addition of CARAFE up-sampling operator, YOLOv8-DC adds the CW-DRB module on top of YOLOv8-C, and YOLOv8-DCP adds the small-target detection layer on top of YOLOv8-DC, that is, the algorithmic model of this paper. The results of the ablation experiments are shown in

Table 4. Results of YOLOv8 DCP ablation experiment.

Model	CARAFE	CW-DRB	P2	[email protected]/%	AP%				P/%	FPS
					ins.	def.	bro.	fla.
YOLOv8n	×	×	×	90.8	92.8	84.2	96.8	89.3	93.8	157
YOLOv8-C	√	×	×	91.2	93.2	84.6	97.1	90.1	95.2	149
YOLOv8-DC	√	√	×	93.2	93.6	91.1	98.2	89.9	95.4	141
YOLOv8-DCP	√	√	√	93.9	94.1	92.7	98.7	90.3	97.7	126

Note: ins. stands for insulator; def. stands for defect; bro. stands for broke; fla. stands for flash.

.

Analysis of the results in Table 4 shows that the [email protected] value of YOLOv8-C improves by 0.4% compared to the baseline model verifying the effectiveness of CARAFE in replacing the sampling layer on the features and strengthening the model’s ability to reconstruct the feature map. The [email protected] value of YOLOv8-DC is improved by 2.4%, which indicates that the introduction of the CW-DRB module can improve the insulator fault diagnosis accuracy and verifies that the module can enhance the model’s ability to extract feature information and fuse contextual semantic information. The [email protected] value of YOLOv8-DCP is improved by 3.1% of the [email protected] value, and after the introduction of the small target detection layer, the flashover fault detection accuracy is improved by 1% compared to the baseline model, which verifies the effectiveness of the module. In summary, the ablation experiment results verify the effectiveness of each improved module of YOLOv8-DCP, as well as the effectiveness of the algorithm in improving insulator fault diagnosis accuracy.

In order to further verify the effectiveness of the improved module on performance enhancement, other current mainstream algorithm models are used to verify the performance comparison with the YOLOv8-DCP algorithm, and the verification results are shown in

Table 5. Training results of different algorithm models.

Model	P/%	R/%	[email protected]/%	FPS
YOLOv5n	96.2	84.3	90.9	147
YOLOv7-tiny	93.4	84.3	89.6	164
YOLOv8n	93.8	86.8	90.8	157
YOLOv8-DCP	97.7	86.9	93.9	126

, while the other performance parameters of each model are compared with the curve diagram in Figure 11.

By analyzing Table 5 and Figure 11, YOLOv8-DCP shows improvements over YOLOv5n, YOLOv7-tiny, and YOLOv8n in terms of precision, recall, and [email protected]. Specifically, YOLOv8-DCP improves precision by 1.5%, 4.3%, and 4.8%, respectively, indicating a stronger ability to accurately identify positive samples. In terms of recall, YOLOv8-DCP improves by 2.6%, 2.6%, and 0.1%, suggesting a lower false negative rate and reduced likelihood of incorrectly classifying positive samples as negative. For [email protected], YOLOv8-DCP achieves improvements of 3.0%, 4.3%, and 3.1%, demonstrating superior overall performance. YOLOv8-DCP not only accurately identifies various types of targets in the dataset but also converges faster and with less fluctuation in the [email protected] curve. In summary, YOLOv8-DCP outperforms other mainstream algorithms in all performance metrics, validating its robustness and the effectiveness of the proposed improvements as demonstrated by ablation and comparative experiments.

3.5. Visualization of YOLOv8-DCP Algorithm Fault Diagnosis Results

In order to compare the fault diagnosis results of the algorithms in different cases, representative images were selected for detection. Infrared images are also added for detection to verify the ability of the model to detect insulators in infrared images. The infrared image of the insulator is to display the heat distribution on the surface of the insulator in a form visible to the human eye through infrared equipment. This image can reveal temperature information about the surface states of the insulator that cannot be obtained from traditional visible light images. When an insulator is broken, flashed over, self-explodes, or has other faults, the insulation performance of the insulator will be reduced, resulting in abnormal local heating. The higher the temperature, the brighter the insulator will appear in the image. Therefore, if the area where the insulator exists in the infrared image can be identified, it will be helpful for maintenance personnel to further judge the status of the insulator, so as to find possible faults in time. Four representative insulator fault images are selected for diagnosis and the results are shown in Figure 12.

As shown in Figure 12a, when the self-explosion location is confused with the image background, the YOLOv8-DCP algorithm recognizes the self-explosion faults with an [email protected] value of 83%, whereas YOLOv8nly 61%, which verifies the effectiveness of the YOLOv8-DCP model for Feature extraction and fusion. As shown in Figure 12b,c, YOLOv8n appears to miss detecting the flash and wrongly detecting the breakage, while the YOLOv8-DCP model is able to accurately diagnose the flash faults at different scales of the image and the accuracy is higher than that of YOLOv8n, which verifies the effectiveness of the algorithm in this paper to add a small target layer. As shown in Figure 12d, the YOLOv8-DCP algorithm accurately identifies infrared insulators with higher accuracy than YOLOv8n. Therefore, the visualized diagnostic results verify that the overall diagnostic accuracy of the YOLOv8-DCP algorithm is higher, and there is no omission or misdetection, which shows that YOLOv8-DCP is more suitable for insulator fault diagnosis in real working conditions.

3.6. Insulator Fault Diagnosis System Development and Result Visualization

To validate the practical utility of the algorithm and verify whether the diagnostic system can function as intended, the YOLOv8-DCP algorithm was deployed within the diagnostic system developed for this study. The diagnostic system, built using the PySide6 framework, supports image, video, and video stream detection, and also includes features for changing detection models and adjusting model parameters. This experiment aims to determine if YOLOv8-DCP can integrate with the diagnostic system interface effectively. By importing the trained YOLOv8-DCP model, we tested whether YOLOv8 can accurately detect objects in images and display the results on the diagnostic system interface. The results of testing images with the trained model in the diagnostic system are shown in Figure 13.

As shown in Figure 13, the system is capable of providing a comparison between the original image and the detection results. The system diagnosed the image with three categories and a total of ten targets: six instances of flashover, one instance of breakage, and one insulator string. The frame rate display, intended for video or video stream detection, is not applicable for a single image. This experiment validates that the fault diagnosis system developed in this study can achieve its intended functionality and that the YOLOv8-DCP algorithm integrates effectively with the diagnostic system.

4. Discussion

Our study demonstrates that YOLOv8-DCP significantly improves the accuracy of detecting insulator faults, including self-explosions, breakage, and flashovers, compared to the original YOLOv8 model. This enhancement enables more effective diagnosis of faults present in insulator images. Additionally, our work facilitates the shift from manual tower inspections to automated methods, thereby increasing the safety of electrical inspections and advancing the intelligence and safety of insulator fault diagnosis. Nevertheless, some insulator faults remain undetected. Possible reasons include the following three factors: first, image quality issues arise due to varying weather conditions and times of image capture, causing the same fault to exhibit different features in the images. Second, some faults are highly confused with the background, leading to errors in detecting faults as background in the detection process. Third, the limitations are related to YOLOv8-DCP itself. While YOLOv8-DCP has improved the detection of small targets compared to the original YOLOv8, it still faces challenges when diagnosing faults that closely resemble their surrounding environment. Additionally, during real-time detection, weather conditions also present a significant challenge for the model. Adverse weather can significantly impact image quality; for instance, heavy rain can cause images to become blurred and thus make it difficult to identify insulators. In heavy snowfall, both the camera and the insulator might be covered, leading to situations where images cannot be captured at all. Therefore, future research will focus on expanding the diversity of image data to ensure that images are sufficiently varied across different weather conditions and times of day. This approach aims to improve the performance of the model so that it can handle insulator fault diagnosis tasks in different weather, time periods, and temperature conditions.

Moreover, compared to YOLOv8, YOLOv8-DCP demonstrates substantial improvements in accuracy, recall, and [email protected]. However, these enhancements also result in increased inference time. Although we opted for parameter-efficient modules like CARAFE to minimize this impact, the complexity of the model inevitably increased. Experimental results indicate that YOLOv8-DCP has longer inference times compared to other mainstream algorithms, which reduces the model’s detection speed. Consequently, deploying this model in practical applications requires higher hardware specifications, potentially limiting its suitability for tasks with stringent real-time requirements. Future work will focus on model optimization and lightweight design to enable rapid deployment and real-time insulator fault detection.

The use of the CW-DRB module provides feasibility for future model updates and fine-tuning. For instance, CW-DRB supports cross-layer feature fusion across different levels. When incorporating the CARAFE up-sampling module, efforts were made to minimize disruption to the original YOLOv8 architecture, thereby preserving the potential for future model enhancements. Additionally, the integration of the proposed algorithm with the diagnostic system provides potential for future mobile deployment. Finally, the model’s reliance on image data for diagnostics and its integration with the fault diagnosis system indicate its potential for establishing a comprehensive visual recognition system. In practical applications, when deployed with hardware devices capable of image acquisition, such as drones, the model can conduct insulation fault diagnostics in complex terrains. For example, integrating with a drone-based image capture system allows for the acquisition of image data in challenging environments, enabling accurate insulation fault diagnosis and transmitting results back to operators. This capability significantly enhances the safety of power inspection processes.

5. Conclusions

Given the diversity of insulator types, complex backgrounds, and the small size of faults, as well as the challenges faced by existing deep learning models in accurately diagnosing insulator faults. To improve upon the YOLOv8 baseline model, we propose the YOLOv8-DCP insulator fault diagnosis algorithm. The YOLOv8-DCP algorithm enhances the YOLOv8 baseline model by integrating the CW-DRB module, which improves the model’s feature extraction and information fusion capabilities. Additionally, the introduction of the content-aware feature module CARAFE ensures that the reconstructed feature maps retain rich information, effectively addressing the issue of insufficient feature details during the up-sampling process. Finally, an extra small-fault detection layer is added to improve the model’s precision in detecting small faults. Experimental results demonstrate that the YOLOv8-DCP model, incorporating the CW-DRB module, achieves improvements in [email protected] compared to other C2f enhancement modules (C2f-ODConv, C2f-Faster, C2f-DySnakeConv), with increases of 1.2%, 1.8%, and 0.8% respectively. Ablation and comparative experiments show that YOLOv8-DCP exhibits overall performance enhancements over the baseline YOLOv8, with precision increased by 4.8%, recall improved by 0.1%, and [email protected] elevated by 3.1%. Additionally, when compared to YOLOv5 and YOLOv7, YOLOv8-DCP demonstrates superior performance metrics, particularly with [email protected] increasing by 3.0% and 4.3%, respectively. These results highlight the effectiveness and superiority of the YOLOv8-DCP algorithm. In addition, the preset functions of the fault diagnosis system were also successfully implemented. The YOLOv8-DCP algorithm integrates seamlessly with the diagnostic system, accurately identifying the types and quantities of faults within images, thereby confirming the practical applicability of the proposed algorithm.

In summary, the improved algorithm proposed in this paper is effective and achieves higher accuracy in insulator fault diagnosis compared to other mainstream algorithms. This advancement is crucial for the intelligent maintenance of power grids and ensuring the safety of power transmission. It also contributes significantly to the intelligent inspection and maintenance of electrical infrastructure, enhancing the overall safety of power transmission systems. In the future, the research will focus on three main directions: the first direction is to expand the diversity of image data, ensuring that the dataset is sufficiently rich across different weather conditions and times of day, which will further enhance the model’s generalization capability. The second direction is to focus on model lightweighting, enabling the model to be quickly deployed on resource-constrained devices for real-time detection of insulator faults. The third direction is to develop a comprehensive fault diagnosis system that operates effectively in various complex and climatic conditions. This involves enhancing the model’s ability to detect insulator defects, ensuring round-the-clock, real-time fault diagnosis.