An insulator self-blast detection method based on YOLOv4 with aerial images

He, Hui; Huang, Xile; Song, Yuxuan; Zhang, Zheng; Wang, Meng; Bo, Chen; Yan, Guangwei

doi:10.1016/j.egyr.2021.11.115

Cited by 19 publications

(12 citation statements)

References 12 publications

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“…The deep learning framework is Pytorch. Six detection methods that our proposed method, YOLOv3 [ 10 ], YOLOv4 [ 11 ], YOLOv5s [ 9 ], Fast R-Transformer [ 38 ], and Mina-Net [ 31 ] method are set with the same parameters and pre-training weights are used. The initial learning rate was set to 0.001, and the learning rate was dynamically adjusted using the cosine annealing learning rate.…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…e deep learning framework is Pytorch. Six detection methods that our proposed method, YOLOv3 [10], YOLOv4 [11], YOLOv5s [9], Fast R-Transformer [38], and Mina-Net [31] method are set with the same parameters and pre-training weights are used.…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…It had a faster detection speed than YOLOv4. He et al [ 31 ] proposed a self-exploding insulator detection algorithm based on YOLOv4. They proposed a new feature fusion structure and an improved SE attention mechanism, which effectively suppressed useless features and achieved higher detection accuracy than YOLOv4.…”

Section: Related Workmentioning

confidence: 99%

“…Methods. We compare our complete proposed method with YOLOv3, YOLOv4, and YOLOv5s, Fast R-Transformer, and [38] Mina-Net [31]. We randomly select three different insulators that contain defective insulators.…”

Section: Comparisons For Differentmentioning

confidence: 99%

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Fast Detection of Defective Insulator Based on Improved YOLOv5s

Zhao

Mengjun

Cui

et al. 2022

Computational Intelligence and Neuroscience

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Defective insulator detection is an essential part of transmission line inspections based on unmanned aerial vehicles. It can timely discover insulator defects and repair them to avoid a power transmission accident. The detection speed of defective insulators based on artificial intelligence directly affects inspection efficiency. To improve the detection speed of defective insulators based on YOLOv5s, an improved detection method with faster detection speed and acceptable precision is proposed. First, a new ResNet unit with three branches is designed based on depthwise separable convolution with kernel three and average pooling. To reduce parameters, the new ResNet unit is used to replace the original ResNet unit used in the CSP1_X module in YOLOv5s. Besides, we also introduce channel shuffle in the CSP1_X module to facilitate the flow of feature information from different channels. Second, a new residual CBL module is designed based on depthwise separable and standard convolution. The new residual CBL module is used to replace the two CBL modules used in the CSP2_X module in YOLOv5s to reduce parameters and extract more useful features. Third, we design a separate, coordinated attention module by introducing location information into channel attention. The new attention module is added to the end of the CSP2_X module to improve the ability to extract insulator location information. Besides, we also use convolution to replace the focus model to reduce computation. Compared with defective insulator detection methods, the proposed method has smaller parameters, floating-point operations per second, and higher frames per second. Although it has lower mean precision, it has a faster detection speed. Besides, the increase in detection speed is greater than the decrease in mean precision.

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Section: Simulation and Discussionmentioning

confidence: 99%

Section: Simulation and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparisons For Differentmentioning

confidence: 99%

See 2 more Smart Citations

Fast Detection of Defective Insulator Based on Improved YOLOv5s

Zhao

Mengjun

Cui

et al. 2022

Computational Intelligence and Neuroscience

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“…Ref. [ 26 ] proposed an insulator self-explosion detection method based on YOLOv4 that, first, fused the shallow feature map into the feature pyramid, and then adopted the SENet structure to improve the recognition accuracy of network. This method had high accuracy and a slight decrease in detection speed.…”

Section: Introductionmentioning

confidence: 99%

Research on Insulator Defect Detection Based on an Improved MobilenetV1-YOLOv4

Deng

Huang

et al. 2022

Entropy

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Insulator devices are important for transmission lines, and defects such as insulator bursting and string loss affect the safety of transmission lines. In this study, we aim to investigate the problems of slow detection speed and low efficiency of traditional insulator defect detection algorithms, and to improve the accuracy of insulator fault identification and the convenience of daily work; therefore, we propose an insulator defect detection algorithm based on an improved MobilenetV1-YOLOv4. First, the backbone feature extraction network of YOLOv4 ‘Backbone’ is replaced with the lightweight module Mobilenet-V1. Second, the scSE attention mechanism is introduced in stages of preliminary feature extraction and enhanced feature extraction, sequentially. Finally, the depthwise separable convolution substitutes the 3 × 3 convolution of the enhanced feature extraction network to reduce the overall number of network parameters. The experimental results show that the weight of the improved algorithm is 57.9 MB, which is 62.6% less than that obtained by the MobilenetV1-YOLOv4 model; the average accuracy of insulator defect detection is improved by 0.26% and reaches 98.81%; and the detection speed reaches 190 frames per second with an increase of 37 frames per second.

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DBFN: Double Branch Fusion Network for Vital Components and Defect Detection of Transmission Line

Chu

Yang

2023

Advcd Theory and Sims

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A double branch fusion network is proposed based on unmanned aerial vehicle (UAV) inspection images to increase the detection accuracy of vital components and defects in transmission lines. The backbone feature extraction network comprises a combination of a convolutional neural network (CNN) and a Transformer network. To be specific, the CNN should extract local information, and the Transformer network is responsible for the extraction of global information. Besides, global information and local information have semantic differences, while resulting in feature aliasing after fusion. To solve this problem, a multiscale convolution module and a multiscale pooling module are proposed to solve semantic differences and feature aliasing through the interaction between two types of information. In general, the enhanced feature extraction network comprises a residual‐like convolution module, which can reduce the loss of detailed information (e.g., edge contours) and further extract high‐level semantic information from the deep network. Besides, it performs feature fusion in multiple regions in the enhanced feature extraction network, such that the multi‐scale adaptability of the neural network is effectively enhanced. Last, the fused feature information at different scales is decoded, and the final detection results are yielded.

show abstract

An insulator self-blast detection method based on YOLOv4 with aerial images

Cited by 19 publications

References 12 publications

Fast Detection of Defective Insulator Based on Improved YOLOv5s

Fast Detection of Defective Insulator Based on Improved YOLOv5s

Research on Insulator Defect Detection Based on an Improved MobilenetV1-YOLOv4

DBFN: Double Branch Fusion Network for Vital Components and Defect Detection of Transmission Line

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