A lightweight YOLOv5 model for small insulator defect detection

Na Liu; Fangzheng Peng; Sheng Hua; Chonghao Yue; Li Dong

doi:10.1117/12.3024278

13 May 2024 A lightweight YOLOv5 model for small insulator defect detection

Na Liu, Fangzheng Peng, Sheng Hua, Chonghao Yue, Li Dong

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 13159AF (2024) https://doi.org/10.1117/12.3024278
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

To address the issues of low accuracy and poor real-time performance in insulator defect detection in power transmission systems, we propose a lightweight insulator defect detection model based on YOLOv5. Firstly, we have introduced a separable vision transformer (SepViT) module to replace several CBS and C3 modules in the final layers of the backbone network, which can greatly reduce network computational complexity and improve detection speed without losing detection accuracy. Secondly, we introduced a bidirectional feature pyramid network (BiFPN) to address the issue of information loss in the original network structure. Finally, we replaced the CBS module in the Neck network with an improved DSC module to reduce the network load again and introduced an Involution block to compensate for the problem of DSC channel information loss. The experimental results show that our model has achieved better performance compared to other insulator detection models, especially for the original YOLOv5s model. Our detection accuracy has been improved by 6.6%, the model size has been reduced by 24.3%, and the detection speed has been improved by 240%, reaching 70.4fps.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Na Liu, Fangzheng Peng, Sheng Hua, Chonghao Yue, and Li Dong "A lightweight YOLOv5 model for small insulator defect detection", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 13159AF (13 May 2024); https://doi.org/10.1117/12.3024278

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KEYWORDS

Defect detection

Object detection

Convolution

Inspection

Performance modeling

Transformers

Head

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