Insulator Breakage Detection Based on Improved YOLOv5

Gujing Han, Min He, Mengze Gao, Jinyun Yu, Kaipei Liu and Liang Qin
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Gujing Han: School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China
Min He: School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China
Mengze Gao: School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China
Jinyun Yu: School of Electrical and Automation, Wuhan University, Wuhan 430072, China
Kaipei Liu: School of Electrical and Automation, Wuhan University, Wuhan 430072, China
Liang Qin: School of Electrical and Automation, Wuhan University, Wuhan 430072, China

Sustainability, 2022, vol. 14, issue 10, 1-17

Abstract: Aerial images have complex backgrounds, small targets, and overlapping targets, resulting in low accuracy of intelligent detection of overhead line insulators. This paper proposes an improved algorithm for insulator breakage detection based on YOLOv5: The ECA-Net (Efficient Channel Attention Network) attention mechanism is integrated into its backbone feature extraction layer, and the effective distinction between background and target is achieved by increasing the weight of important channels. A bidirectional feature pyramid network is added to the feature fusion layer, and large-scale images with more original information are combined to effectively retain small target features. Incorporating a flexible detection frame selection algorithm Soft-NMS (Soft Non-Maximum Suppression) into the prediction layer to re-screen the target frame, thereby reducing the probability of mistaken deletion of overlapping targets. The effectiveness of the improved YOLOv5 algorithm is verified in the actual aerial image dataset, and the results show that the mean Average Precision (mAP) of the improved algorithm is 95.02% and the detection speed FPS (Frames Per Second) can reach 49.4 frames/s, which meets the real-time and accuracy requirements of engineering applications.

Keywords: insulator; small target detection; YOLOv5; Bi-FPN; ECA-Net; overlapping target detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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