Search Results (179)

To precisely delineate the contours of insulators in complex transmission line images obtained from Unmanned Aerial Vehicle (UAV) inspections and thereby facilitate subsequent defect analysis, this study proposes an instance segmentation framework predicated upon an enhanced SOLOv2 model. The proposed framework integrates a preprocessed edge channel, generated through the Non-Subsampled Contourlet Transform (NSCT), which augments the model’s capability to accurately capture the edges of insulators. Moreover, the input image resolution to the network is heightened to 1200 × 1600, permitting more detailed extraction of edges. Rather than the original ResNet + FPN architecture, the improved HRNet is utilized as the backbone to effectively harness multi-scale feature information, thereby enhancing the model’s overall efficacy. In response to the increased input size, there is a reduction in the network’s channel count, concurrent with an increase in the number of layers, ensuring an adequate receptive field without substantially escalating network parameters. Additionally, a Convolutional Block Attention Module (CBAM) is incorporated to refine mask quality and augment object detection precision. Furthermore, to bolster the model’s robustness and minimize annotation demands, a virtual dataset is crafted utilizing the fourth-generation Unreal Engine (UE4). Empirical results reveal that the proposed framework exhibits superior performance, with AP0.50 (90.21%), AP0.75 (83.34%), and AP[0.50:0.95] (67.26%) on a test set consisting of images supplied by the power grid. This framework surpasses existing methodologies and contributes significantly to the advancement of intelligent transmission line inspection. Full article

Power equipment detection is a critical component in power transmission line inspection. However, existing power equipment detection algorithms often face problems such as large model sizes and high computational complexity. This paper proposes a lightweight power equipment detection algorithm based on large receptive field and attention guidance. First, we propose a lightweight large receptive field feature extraction module, CRepLK, which reparameterizes multiple branches into large kernel convolution to improve the multi-scale detection capability of the model; secondly, we propose a lightweight ELA-guided Dynamic Sampling Fusion (LEDSF) Neck, which alleviates the feature misalignment problem inherent in conventional neck networks to a certain extent; finally, we propose a lightweight Partial Asymmetric Detection Head (PADH), which utilizes the redundancy of feature maps to achieve the significant light weight of the detection head. Experimental results show that on the Insplad power equipment dataset, the number of parameters, computational cost (GFLOPs) and the size of the model weight are reduced by 46.8%, 44.1% and 46.4%, respectively, compared with the Baseline model, while the mAP is improved by 1%. Comparative experiments on three power equipment datasets show that our model achieves a compelling balance between efficiency and detection performance in power equipment detection scenarios. Full article

Transmission lines are widely distributed in complex environments, making them susceptible to foreign object intrusion, which could lead to serious consequences, i.e., power outages. Currently, foreign object detection on transmission lines is primarily conducted through UAV-based field inspections. However, the captured data must be transmitted back to a central facility for analysis, resulting in low efficiency and the inability to perform real-time, industrial-grade detection. Although recent YOLO series models can be deployed on UAVs for object detection, these models’ substantial computational requirements often exceed the processing capabilities of UAV platforms, limiting their ability to perform real-time inference tasks. In this study, we propose a novel lightweight detection algorithm, SCL-YOLOv8, which is based on the original YOLO model. We introduce StarNet to replace the CSPDarknet53 backbone as the feature extraction network, thereby reducing computational complexity while maintaining high feature extraction efficiency. We design a lightweight module, CGLU-ConvFormer, which enhances multi-scale feature representation and local feature extraction by integrating convolutional operations with gating mechanisms. Furthermore, the detection head of the original YOLO model is improved by introducing shared convolutional layers and group normalization, which helps reduce redundant computations and enhances multi-scale feature fusion. Experimental results demonstrate that the proposed model not only improves the detection accuracy but also significantly reduces the number of model parameters. Specifically, SCL-YOLOv8 achieves a mAP@0.5 of 94.2% while reducing the number of parameters by 56.8%, FLOPS by 45.7%, and model size by 50% compared with YOLOv8n. Full article

Thermographic inspections using drones with thermographic cameras have enabled considerable advances in preventive maintenance. In this context, we propose a methodology for prioritizing flight performance to ensure that the equipment is used in the most efficient way, based on the implementation of thermographic technology in the preventive maintenance plans of electric power concessionaires. Based on information about transmission lines obtained from the literature and made available by transmission companies, criteria and alternatives are established, and a methodology for prioritization and application in transmission lines is established using the AHP multicriteria method. Technical, safety, systemic, social, and financial criteria are defined, each containing alternatives, to define their weight and importance. Finally, through analysis of the four established criteria and alternatives, with their respective weights, a tool is obtained that will assist transmission concessionaires in the adequate prioritization of thermographic inspections using RPAS. Full article

Pipelines, as critical infrastructure in energy transmission, municipal facilities, industrial production, and specialized equipment, are essential to national economic security and social stability. This paper systematically reviews the domestic and international research status of pipeline in-line inspection (ILI) technologies, with a focus on four major technological systems: electromagnetic, acoustic, optical, and robotic technologies. The operational principles, application scenarios, advantages, and limitations of each technology are analyzed in detail. Although existing technologies have achieved significant progress in defect detection accuracy and environmental adaptability, they still face challenges including insufficient adaptability to complex environments, the inherent trade-off between detection accuracy and efficiency, and high equipment costs. Future research directions are identified as follows: intelligent algorithm optimization for multi-physics collaborative detection, miniaturized and integrated design of inspection devices, and scenario-specific development for specialized environments. Through technological innovation and multidisciplinary integration, pipeline ILI technologies are expected to progressively realize efficient, precise, and low-cost lifecycle safety monitoring of pipelines. Full article

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations. Full article

Power transmission infrastructure requires continuous inspection to prevent failures and ensure grid stability. UAV-based systems, enhanced with deep learning, have emerged as an efficient alternative to traditional, labor-intensive inspection methods. However, most existing approaches rely on separate models for cable segmentation and anomaly detection, leading to increased computational overhead and reduced reliability in real-time applications. To address these limitations, we propose PowerLine-MTYOLO, a lightweight, one-stage, multitask model designed for simultaneous power cable segmentation and broken strand detection from UAV imagery. Built upon the A-YOLOM architecture, and leveraging the YOLOv8 foundation, our model introduces four novel specialized modules—SDPM, HAD, EFR, and the Shape-Aware Wise IoU loss—that improve geometric understanding, structural consistency, and bounding-box precision. We also present the Merged Public Power Cable Dataset (MPCD), a diverse, open-source dataset tailored for multitask training and evaluation. The experimental results show that our model achieves up to +10.68% mAP@50 and +1.7% IoU compared to A-YOLOM, while also outperforming recent YOLO-based detectors in both accuracy and efficiency. These gains are achieved with a smaller model memory footprint and a similar inference speed compared to A-YOLOM. By unifying detection and segmentation into a single framework, PowerLine-MTYOLO offers a promising solution for autonomous aerial inspection and lays the groundwork for future advances in fine-structure monitoring tasks. Full article

Composite insulators are deployed globally for outdoor insulation owing to their light weight, excellent pollution resistance, good mechanical strength, ease of installation, and low maintenance costs. The core rod in composite long-rod insulators plays a critical role in both mechanical load-bearing and internal insulation for overhead transmission lines, and its performance directly affects the overall operational condition of the insulator. However, it remains susceptible to failures induced by complex actions of mechanical, electrical, thermal, and environmental stresses. This paper systematically reviews the major failure modes of core rods, including mechanical failures (normal fracture, brittle fracture, and decay-like fracture) and electrical failures (flashunder and abnormal heating of the core rod). Through analysis of extensive field data and research findings, key failure mechanisms are identified. Preventive strategies encompassing material modification (such as superhydrophobic coatings, self-diagnostic materials, and self-healing epoxy resin), structural optimization (like the optimization of grading rings), and advanced inspection methods (such as IRT detection, Terahertz (THz) detection, X-ray computed tomography (XCT)) are proposed. Furthermore, the limitations of current technologies are discussed, emphasizing the need for in-depth studies on deterioration mechanisms, materials innovation, and defect detection technologies to enhance the long-term reliability of composite insulators in transmission networks. Full article

To address the challenges of high missed detection rates for minute transmission line defects, strong complex background interference, and limited computational power on edge devices in UAV-assisted power line inspection, this paper proposes a lightweight improved YOLOv12 real-time detection model. First, a Bidirectional Weighted Feature Fusion Network (BiFPN) is introduced to enhance bidirectional interaction between shallow localization information and deep semantic features through learnable feature layer weighting, thereby improving detection sensitivity for line defects. Second, a Cross-stage Channel-Position Collaborative Attention (CPCA) module is embedded in the BiFPN’s cross-stage connections, jointly modeling channel feature significance and spatial contextual relationships to effectively suppress complex background noise from vegetation occlusion and metal reflections while enhancing defect feature representation. Furthermore, the backbone network is reconstructed using ShuffleNetV2’s channel rearrangement and grouped convolution strategies to reduce model complexity. Experimental results demonstrate that the improved model achieved 98.7% mAP@0.5 on our custom transmission line defect dataset, representing a 3.0% improvement over the baseline YOLOv12, with parameters compressed to 2.31M (8.3% reduction) and real-time detection speed reaching 142.7 FPS. This method effectively balances detection accuracy and inference efficiency, providing reliable technical support for unmanned intelligent inspection of transmission lines. Full article

In power inspection fields, snake-like robots are often used for transmission line inspection tasks, requiring accurate identification of various power fittings. However, traditional visual sensors perform poorly under varying light intensity and complex background conditions. This paper proposes a non-visual perception method for the high-precision classification of different power fittings (e.g., vibration dampers, suspension clamps, and tension clamps) in snake-like robot transmission line inspection for high-voltage lines. This method, unaffected by light intensity changes, uses machine learning to classify the magnetic induction electromotive force signals around the fittings. First, the Dodd–Deeds eddy current model is used to analyse the magnetic field changes around the transmission line fittings and determine the induction coil distribution. Then, the concept of condition number and singular value decomposition (SVD) are introduced to analyse the impact of detection position on classification accuracy, with optimal detection positions found using the particle swarm optimization algorithm. Finally, a BP neural network optimised by a genetic algorithm is used for power fitting identification. Experiments show that this method successfully identifies vibration dampers, tension clamps, suspension clamps, and transmission lines at detection distances of 5 cm, 10 cm, 15 cm, and 20 cm, with accuracies of 99.8%, 97.5%, 95.1%, and 92.5%, respectively. Full article

Power line segmentation plays a critical role in ensuring the safety of transmission line UAV inspection flights. To address the challenges of small target scale, complex backgrounds, and excessive model parameters in existing deep learning-based power line segmentation algorithms, this paper introduces RGS-UNet, a lightweight segmentation model integrating a residual-like cross-layer feature fusion module. First, ResNet18 is adopted to reconstruct a UNet backbone network as an encoder module to enhance the network’s feature extraction capability for small targets. Second, ordinary convolution in the residual block of ResNet18 is optimized by introducing the Ghost Module, which significantly reduces the computational load of the model’s backbone network. Third, a residual-like addition method is designed to embed the SIMAM attention mechanism module into both encoder and decoder stages, which improves the model’s ability to extract power lines from complex backgrounds. Finally, the Mish activation function is applied in deep convolutional layers to maintain feature extraction accuracy and mitigate overfitting. Experimental results demonstrate that compared with classical UNet, the optimized network achieves 2.05% and 2.58% improvements in F1-Score and IoU, respectively, while reducing the parameter count to 57.25% of the original model. The algorithm achieves better performance improvements in both accuracy and lightweighting, making it suitable for edge-side deployment. Full article

We developed a fully automated key production line for smart manufacturing technologies based on the Internet of Things (IoT) and automatic optical inspection (AOI) to enable efficient and consistent production. The production line consists of seven processing stations: raw materials uploading, groove milling, laser marking, key tooth cutting, deburring, defects inspection, and a discharge station. IoT technology enables real-time monitoring and data transmission through a visual panel that displays the operational status of each station and provides immediate alerts in case of abnormalities for quick intervention. The defects inspection station ensures comprehensive quality checks, automatically stops the production line for detected defects, and prevents defective products from proceeding to subsequent stages. Chronological data are used to support predictive maintenance, production parameter optimization, and energy efficiency improvements. Overall, the system effectively integrates automation, real-time monitoring, and quality control to ensure stable production and high product quality. Full article

Efficient insulator-defect detection in transmission lines is crucial for ensuring the reliability and safety of power systems. This study introduces YOLOv8-IDX (You Only Look Once v8—Insulator Defect eXtensions), an enhanced DL (Deep Learning) based model designed specifically for detecting defects in transmission line insulators. The model builds upon the YOLOv8 framework, incorporating advanced modules, such as C3k2 in the backbone for enhanced feature extraction and C2fCIB in the neck for improved contextual understanding. These modifications aim to address the challenges of detecting small and complex defects under diverse environmental conditions. The results demonstrate that YOLOv8-IDX significantly outperforms the baseline YOLOv8 in terms of mean Average Precision (mAP) by 4.7% and 3.6% on the IDID and CPLID datasets, respectively, with F1 scores of 93.2 and 97.2 on the IDID and CPLID datasets, respectively. These findings underscore the model’s potential in automating power line inspections, reducing manual effort, and minimizing maintenance-related downtime. In conclusion, YOLOv8-IDX represents a step forward in leveraging DL and AI for smart grid applications, with implications for enhancing the reliability and efficiency of power transmission systems. Future work will focus on extending the model to multi-class defect detection and real-time deployment using UAV platforms. Full article

Accurate detection and timely management of high-voltage transmission accessories are crucial for ensuring the safe operation of power transmission. Existing network models suffer from issues like low precision in accessory detection, elevated model complexity, and a narrow range of category detection, especially in UAV-based inspection scenarios. To alleviate the above problems, we propose an innovative Weight Coordinated Adaptive Network (WCANet) in this paper, aiming to improve the efficiency and accuracy of high-voltage transmission accessories detection. The network is designed with a plug-and-play WCA module that can effectively identify dense small targets, retain information in each channel, and reduce computational overheads, while incorporating Sim-AFPN with a skip-connection structure into the network aggregate feature information layer by layer, enhancing the ability to capture key features, and achieving a lightweight network structure. The WIoU loss of bounding box regression (BBR) is to reduce the competitiveness of high-quality anchor boxes and mask the effects of the low-quality examples, thus improving the accuracy of the model. The experimental results show that WCANet has achieved remarkable results in the HVTA, VisDrone2019, and VOC2007 datasets. Compared with other methods, our WCANet achieves highly accurate prediction of high-voltage transmission accessories with fewer parameters and model sizes, availably balancing model performance and complexity. Full article

Regular inspection of overhead power line (OPL) systems is required to detect damage early and ensure the efficient and uninterrupted transmission of high-voltage electric power. In the past, these checks were conducted utilizing line crawling, inspection robots, and a helicopter. Yet, these traditional solutions are slow, costly, and hazardous. Advancements in drones, edge computing platforms, deep learning, and high-resolution cameras may enable real-time OPL inspections using drones. Some research has been conducted on OPL inspection with autonomous drones. However, it is essential to explore how to achieve real-time OPL component detection effectively and efficiently. In this paper, we report our research on OPL component detection on edge computing devices. The original OPL dataset is generated in this study. In this paper, we evaluate the detection performance with several sizes of training datasets. We also implement simple data augmentation to extend the size of datasets. The performance of the YOLOv7 model is also evaluated on several edge computing platforms, such as Raspberry Pi 4B, Jetson Nano, and Jetson Orin Nano. The model quantization method is used to improve the real-time performance of the detection model. The simulation results show that the proposed YOLOv7 model can achieve mean average precision (mAP) over 90%. While the hardware evaluation shows the real-time detection performance can be achieved in several circumstances. Full article