Insulator Defects Detection for Aerial Photography of the Power Grid Using You Only Look Once Algorithm

Wang, Shanshan; Zou, Xinyi; Zhu, Wei; Liang, Zhou

doi:10.1007/s42835-023-01385-3

Cited by 10 publications

(10 citation statements)

References 28 publications

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“…While various experiments have evaluated different combinations of modified substructures and compared multiple baseline structures, a comparison between MI-YOLO and SOTA methods for detection remains highly necessary. Therefore, 10 SOTA methods based on improved YOLO, 30 – 37 improved SSD, 52 and improved faster R-CNN 53 are included in this experiment. Relative results are presented in Table 10.…”

Section: Methodsmentioning

confidence: 99%

“…Han et al 34 combined the self-attention mechanism and ECA-Net to modify YOLOv4-Tiny, achieving a remarkable decrease in the calculation for insulator detection and damage identification. Wang et al 35 used MobileNet-v2 to replace the backbone network of YOLOv4 while adding a CBAM attention mechanism to the backbone and an ultra-lightweight subspace attention module to the head to increase channel information. Ru et al 36 embedded an efficiency channel attention (ECA) module into the connection between backbone and neck in YOLOX-Tiny and also appended ECA to the end of the neck to improve detection effectiveness.…”

Section: Introductionmentioning

confidence: 99%

“…combined the self-attention mechanism and ECA-Net to modify YOLOv4-Tiny, achieving a remarkable decrease in the calculation for insulator detection and damage identification. Wang et al 35 . used MobileNet-v2 to replace the backbone network of YOLOv4 while adding a CBAM attention mechanism to the backbone and an ultra-lightweight subspace attention module to the head to increase channel information.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MI-YOLO: more information based YOLO for insulator defect detection

Luan

et al. 2023

J. Electron. Imag.

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Insulators that connect high-voltage transmission lines may experience various faults due to long-term exposure to the natural environment, leading to safety degradation and reliability issues in power grids. Therefore, detecting defective insulators through daily maintenance and long-term overhaul is crucial. We propose an insulator defect detection method to achieve this objective, using a novel neural network named more information-you only look once (MI-YOLO). MI-YOLO includes several modifications compared to YOLOv5s: a skip connection module with downsampling in the backbone, a spatial pyramid dilated convolution module in the neck, and a novel serial-parallel spatial-channel attention module in between. These changes enhance the feature abstraction process by providing more information (MI) through the YOLO-like structure, hence the name MI-YOLO. To evaluate the superiority of MI-YOLO, multiple experiments are performed. First, an ablation study demonstrates the effectiveness of novel substructures and their combinations compared to YOLOv5s. Then, MI-YOLO is compared to baseline structures, including faster R-CNN, SSD, and five YOLOs. Furthermore, the performances of MI-YOLO and 10 state-of-the-art object detection methods are evaluated. Lastly, a second dataset is adopted to evaluate the generalization property and heat maps are presented. The experimental results show that the new designs substantially improve the accuracy of insulator fault detection by abstracting more information from the features.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MI-YOLO: more information based YOLO for insulator defect detection

Luan

et al. 2023

J. Electron. Imag.

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“…In terms of traditional image processing methods, Muhammad Tariq et al [ 1 ] used morphological techniques to detect and identify insulators in power distribution systems. By performing morphological filtering, edge detection, and connectivity analysis on the images, insulators can be accurately detected and identified; Xiaobo Wang et al [ 2 ] proposed a method for insulator detection in power transmission lines using computer vision techniques in 2017. Automatic detection and localization of insulators were achieved by image processing techniques, including image preprocessing, binarization, morphological operations, and connected region analysis; Alaa H. Hussein et al [ 3 ] used digital image processing techniques for insulator detection in high-voltage transmission lines.…”

Section: Related Workmentioning

confidence: 99%

Insulator Abnormal Condition Detection from Small Data Samples

Wang,

Fan,

Luan

et al. 2023

Sensors

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Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system’s normal operation, security, and dependability. Traditional insulator detection methods, on the other hand, necessitate a significant amount of labor and material resources, necessitating the development of a new detection method to substitute manpower. This paper investigates the abnormal condition detection of insulators based on UAV vision sensors using artificial intelligence algorithms from small samples. Firstly, artificial intelligence for the image data volume requirements was large, i.e., the insulator image samples taken by the UAV vision sensor inspection were not enough, or there was a missing image problem, so the data enhancement method was used to expand the small sample data. Then, the YOLOV5 algorithm was used to compare detection results before and after the extended dataset’s optimization to demonstrate the expanded dataset’s dependability and universality, and the results revealed that the expanded dataset improved detection accuracy and precision. The insulator abnormal condition detection method based on small sample image data acquired by the visual sensors studied in this paper has certain theoretical guiding significance and engineering application prospects for the safe operation of active distribution networks.

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“…Therefore, it makes all the difference to improve the examination and repair level of transmission lines. UAV inspection has better applicability and security and is extensively applicated in the operation and maintenance of transmission lines [1][2] . Owing to the sophisticated structure of the transmission line and its accessories, there are many patrol parts.…”

Section: Introductionmentioning

confidence: 99%

Path optimizing of UAV inspection for 500kV AC transmission line cup-tower based on ant colony algorithm

Guo,

Liu,

et al. 2023

J. Phys.: Conf. Ser.

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To ensure unmanned aerial vehicles (UAVs) complete the inspection task of a high-voltage transmission line safely and effectively, it is necessary to consider the interference of a complicated electromagnetic field near a high-voltage transmission line and determine a reasonable inspection distance and path of the UAV. Taking the 500kV EHV AC power line as an instance, firstly, the three-dimensional model of the 500kV ZV1 cup-tower is built, and the finite element method (FEM) is used for computing the electric field and magnetic field of the UAV exterior. According to inspection specifications, the electromagnetic field protection threshold of the UAV is determined, and then the safety area between UAV and the split conductor is determined to be equal to 1.5 m. Combined with the UAV shooting field of view and safety distance, the spatial positions of UAV hovering shooting points are selected to ensure that UAV flies in a safe area. Finally, the path of UAV traversing hovering shooting points is optimized by the ant colony algorithm (ACO), and the optimal path distance is 109.4 m.

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Insulator Defects Detection for Aerial Photography of the Power Grid Using You Only Look Once Algorithm

Cited by 10 publications

References 28 publications

MI-YOLO: more information based YOLO for insulator defect detection

MI-YOLO: more information based YOLO for insulator defect detection

Insulator Abnormal Condition Detection from Small Data Samples

Path optimizing of UAV inspection for 500kV AC transmission line cup-tower based on ant colony algorithm

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