1. Introduction
With the development of the economy and society, the demand for energy is increasing. And the wind power is a kind of renewable and widely used around the world 3. Based on the Global Wind Report [
1], in 2022, the global wind power installed capacity added 77.6 GW, the cumulative installed capacity reached 906GW, an increase of 9% over the previous year. However, as the core part of the wind turbine for converting wind energy into mechanical energy, the WTB is also vulnerable to alternating wind speeds and harsh environments such as oceans, deserts, and mountains, and damage to wind turbine blades not only reduces wind power and economic benefits, but also causes blade breakage and casualties [
2]. Thus, the accurate detection of damaged blades is essential for both blade protection and safe turbine operation. In order to solve the problem, numerous techniques for identifying surface damage on WTBs are proposed and have been well applied, such as Acoustic Emission [
3,
4,
5], the Fiber Bragg grating [
6], Vibration [
7,
8,
9], Infrared Thermography [
10,
11], and Ultrasonic Testing [
12,
13,
14].
With the advancement of UAV technology [
15] and image processing technology, the UAV-based image acquisition and damage detection technology have begun to be studied due to their superiorities of having a low cost, their easy operation and the fact that using them is a form of nondestructive testing [
16,
17,
18,
19,
20]. Initially, several techniques for feature extraction coupled with machine learning algorithms were suggested for identifying the types of damage to WTBs, such as Deng et al. [
21], who propose a filter based on the log-Gabor filter and the LPSO algorithm to adaptively extract the optimal feature from damaged images, and use the classifier based on the HOG and the SVM to identify the damage type. Movsessian et al. [
22] propose a robust ANN-based damage detection method with a new index for danage detection. Peng et al. [
23] propose an image enhancement technique that employs cartoon texture decomposition, multi-directional Gabor filtering, and gradient threshold-based segmentation to improve the precision of damage identification. Yang et al. [
24] propose a deep learning network framework where the Otsu thresholding technique is applied for automatic feature extraction from images, and transfer learning along with ensemble learning are employed to boost the precision and efficiency of damage detection. Guo et al. [
25] propose a hierarchical identification framework, in which the Haar-AdaBoost and the CNN classifier are used for detecting damaged blades. Sun et al. [
26] propose a condition monitoring of blades method, in which they only need healthy measurements for training. Liu et al. [
27] propose a lightweight model called YOLOv3-Mobilenet-PK for detecting surface-damaged blades. Foster et al. [
28] propose a pubulic database of damaged blades for detection and compare the performance of some detection models.
Recently, Many attention mechanism strategies have been used to detect damaged WTBs. Zou et al. [
29] propose an improved CNN, in which the attention module SC changed into the DPCI_SC block is used to obtain image spatial position information more efficiently. Zhang et al. [
30] propose a detection method combining MobileNetv1-YOLOv4 and the transfer learning method, in which three different attention-based feature optimization modules are use to adaptively optimize features. Zhang et al. [
31] propose an improved lightweight YOLOv5s network by incorporating the Convolution Block Attention Module (CBAM). Liu et al. [
32] have developed an innovative lightweight model designed for the detection of damaged WTBs by utilizing an attention mechanism, thereby reducing computational time and enhancing detection precision. Chen et al. [
33] propose an improved YOLOv5 framework, in which the CBAM is used to heighten damaged features of blades. Ma et al. [
34] propose the MES-YOLOv8n model with ECA, which has higher accuracy than the primitive YOLOv8n for damaged blade detection.
As the size, shape and position of different types of surface damaged blades in UAV images are different. Specially, in consideration of the drone’s position and shooting angle, a large amount of complex background information will be contained in the UAV image and the damaged blade may only take up a small area in the UAV image. That is, the damage in blades will become smaller comparing with the whole UAV image. Those factors will lower the quality of the UAV image and seriously interfere with the effect of the detection of the damaged blade. In order to enhance the detection ability for low-quality UAV images, this paper proposes an improved YOLOv7 model fusing the ECA module, the DownSample module, the DGST module and the MIoU loss function module. The key contributions are summarized as follows:
As low-quality UAV images increase the difficulty of damage feature extraction, resulting in large computation and the long time consumption of the primitive YOLOv7 framework, the DGST is proposed as a new feature extraction module to replace the ELAN module in the Backbone of the YOLOv7 model and the DownSample module is introduced into the Backbone of the YOLOv7 model to enhance the ability of feature extraction and detection time;
such as cracks and other damage is not easy to be detected. Thus, the ECA attention module is introduced into the Neck of the YOLOv7 to enhance the model’s attention to damaged features and reduce the interference of irrelevant information in damaged images;
In order to solve the problem of mismatch between the generated bounding box and the actual bounding box, the MIoU loss function is used to represent the bounding box information more comprehensively and carefully.
The following sections of this paper are structured as indicated below. In
Section 2, an overview of both the primitive YOLOv7 and the improved YOLOv7 architecture is presented. In
Section 3, assessment indexes are introduced. In
Section 4, the experiment and analysis based on the public blade image database are presented. In
Section 5, the experiment and analysis based on the blade image database from a company are presented. Finally, this paper is summarize in the Conclusion.
3. Assessment Index
In the experiment, three evaluation indicators are used for quantitative measurement, that is, , precision (recored as P), and recall (recorded as R), which can reflect the performance of the improved YOLOv7 algorithm.
The index
represents the mean average precision calculated under an IoU threshold of 0.5, and the
is calculated by Equation (
3),
where, the
n signifies the count of categories, while the
refers to the area bounded by the precision–recall curve for each category and the coordinate axes, indicating the accuracy of the recognition for a single category.
The
P is short for precision and is calculated by Equation (
4):
The
R is short for recall and is calculated by Equation (
5):
where
stands for True Positive, indicating correct positive predictions,
stands for False Positive, indicating incorrect predictions of negative samples as positive, and
stands for False Negative, indicating incorrect predictions of positive samples as negative.
Considering that the detection efficiency is also key to surface damage detection on WTBs, the number of parameters (recorded as
) and the frame rates (recorded as
F) are used in this paper. And, they are, respectively, calculated by Equations (
6) and (
7).
where the
K is used to denote the convolution kernel’s size, the
M is used to denote the next feature map’s size, the
is used to denote the input layer and the
is used to denote the output layer.
where the
N delegates the sample quantity, while the
T denotes the duration taken to test these samples.
6. Conclusions
In this paper, an improved YOLOv7 model with the DGST module, the DownSample module, the ECA module, and the MIoU module is proposed, and it not only advances the detection accuracy, but also advances the robustness for WTB detection based on low-quality images taken by UAVs.
As low-quality UAV images increase the difficulty of damage feature extraction, resulting in the large computation and long time consumption of the primitive YOLOv7 framework, the DGST and the DownSample modules are adopted to build a new feature extraction network, which can capture more abundant feature information and avoid the insufficient damage feature information extraction in the YOLOv7 backbone. What is more, the results of the ablation experiment indicate that our model outperforms the primitive YOLOv7 model with a more than 3.5% improvement in the , P and R indexes.
Because the characteristic information of damage in low-quality images is fuzzy, cracks and other damages are not easy to be detect. Theerefore, the ECA attention module is applied to solve the above question by advancing the model’s focus on damage features and minimizing the impact of intricate and unrelated background details in damage imagery. Moreover, the results from the ablation experiments revealed that our model has boosted the P and R indexes by 7.7% and 6.7%, respectively, thereby diminishing issues related to false positives and false negatives.
The MIoU loss function is used to more accurately and thoroughly represent bounding box information so as to solve the mismatch between the generated and the actual bounding box. Additionally, the results of the ablation experiment indicate that our model outperforms the primitive YOLOv7 model by 2.5% in
[email protected], 2.8% in P, and 1.6% in R, respectively.
Further, based on two blade image databases, the improved YOLOv7 model is compared with the original YOLOv7 model, the YOLOv8l model, the YOLOv9e model, and the YOLOv10x model to verify the detection accuracy, the processing speed and the robustness. For the first database, the index, the P index and the index of our model is the highest among the five models. For the second database, the values of the index and the recall (R) index of our model are the highest. However, the value of the precision (P) index of our model is only 0.7% lower than that of the YOLOv10x model. Furthermore, our model’s frame rate lags behind that of the primitive YOLOv7 model by a mere 0.54 fps. That is, the image processing speed of our model is 1.3 times that of the YOLOv8l model, 2.7 times that of the YOLOv9e model and 1.3 times that of the YOLOv10x model, respectively. Specifically, for the comparative robustness results, each model has 12 values compared under three kinds of criteria and four SNR levels; our model has eleven values that are at the maximum. Thus, synthetically considering the above contrastive analyses shows that our model has the optimal detection performance.