红外技术2024，Vol.46Issue(9)：1035-1042,8.
基于改进YOLOv7的变电站设备红外图像识别方法
Infrared Image Recognition Method of Substation Equipment Based on Improved YOLOv7
摘要
Abstract
Infrared image recognition of substation electrical equipment is an important prerequisite for defect and fault diagnosis to ensure the safe and stable operation of power systems.To realize high-precision and high-efficiency recognition of substation equipment,in this study,an infrared image recognition method of substation equipment is proposed based on an improved YOLOv7 network.The infrared image acquired by the substation is used as the input for the YOLOv7 network.In the recognition of infrared images,a CoordConv convolution layer is used to increase the image coordinate information,enhance the information details of the network layer,and enrich the image feature content.The attention mechanism is introduced to eliminate other information interference,enhance the feature expression ability of the model,and improve the accuracy of network training.To further improve the recognition accuracy,unlike the traditional loss function,the WIoU loss function is used to accelerate the network convergence and improve the model accuracy.By analyzing the actual infrared images acquired by the substation,the experimental results show that the recognition accuracy of the infrared image recognition model of the substation equipment based on the improved YOLOv7 network can reach 97.1%.Compared with the YOLOv7 network and other typical networks,the proposed model has higher accuracy and robustness and can be effectively applied to intelligent monitoring and maintenance of substation equipment,providing basic conditions for subsequent fault diagnosis.关键词
变电站设备/红外图像识别/YOLOv7/CoordConv/注意力机制/WIoUKey words
substation equipment/infrared image recognition/YOLOv7/CoordConv/attention mechanisms/WIoU分类
计算机与自动化引用本文复制引用
陈怡伦,马萍,贾爱迪,张宏立..基于改进YOLOv7的变电站设备红外图像识别方法[J].红外技术,2024,46(9):1035-1042,8.基金项目
新疆维吾尔自治区自然科学基金资助项目(2022D01C367) （2022D01C367）
国家自然科学基金资助项目(复杂数据特征下风电传动系统故障诊断研究,52065064,52267010). （复杂数据特征下风电传动系统故障诊断研究,52065064,52267010）
