电网技术2023，Vol.47Issue(12)：5273-5282,10.DOI:10.13335/j.1000-3673.pst.2022.1438
基于轻量化改进型YOLOv5s的可见光绝缘子缺陷检测算法
Defect Detection Algorithm Based on Lightweight and Improved YOLOv5s for Visible Light Insulators
摘要
Abstract
With the development of smart grids,the aerial insulator defect detection based on computer vision is widely used in power inspection.In this paper,aiming at the low accuracy of the deep learning model for insulator self-explosion defect detection,and the difficult deployment to the mobile devices,such as drones,due to the large model,the YOLOv5s(You Only Look Once Version-5s)model is selected as the basic network to improve the detection accuracy,and the improved network is pruned to lighten the model.First,the SiLU activation function is replaced with the Mish activation function with a better gradient flow to enhance the network stability;second,the CBAM(Convolutional Block Attention Module)attention mechanism is fused into the last layer of the backbone feature extraction network to filter out more useful features;finally,the Transformer coding structure is embedded into the C3 module,and the C3 in the YOLOv5s feature fusion network is replaced with the new C3TR to strengthen the feature fusion capabilities of the high and low-level networks.The comprehensive pruning method is used for the improved model,and the redundant channels and convolution kernels in the network are cut out respectively to make the model more lightened.Through an experimental verification and compared with the current commonly used models on the test set,the detection accuracy of the improved model in this paper reaches up to 97.23％,the size of the model after pruning is only 0.5MB,the detection time is 1.8ms,and the number of floating-point operations is 0.61 G,which better meets the requirements of real-time detection of the transmission lines.关键词
绝缘子/YOLOv5s/剪枝/缺陷检测/注意力机制Key words
insulators/YOLOv5s/pruning/defect detection/attention mechanism分类
动力与电气工程引用本文复制引用
谢静,杜耀文,刘志坚,刘航,王天艺,缪猛..基于轻量化改进型YOLOv5s的可见光绝缘子缺陷检测算法[J].电网技术,2023,47(12):5273-5282,10.基金项目
国家自然科学基金重点项目(52037003) （52037003）
云南省重大科技专项计划项目(202002AF080001) （202002AF080001）
云南省重点研发计划项目(202303AA080002).Project Supported by Key Program of National Natural Science Foundation of China(52037003) （202303AA080002）
Major Science and Technology Program of Yunnan Province(202002AF080001) （202002AF080001）
Key Research Program of Yunnan Province(202303AA080002). （202303AA080002）
