南方电网技术2024，Vol.18Issue(9)：47-58,12.DOI:10.13648/j.cnki.issn1674-0629.2024.09.006
基于改进YOLOv7的输电线路异物检测模型
Foreign Object Detection Model of Transmission Line Based on Improved YOLOv7
摘要
Abstract
Aiming at the problems of background interference,low image resolution,and large scale variations of foreign objects in the detection of foreign objects on power transmission lines,a foreign object detection model of power transmission line based on improved YOLOv7 is proposed.Firstly,a new backbone network is constructed through space to depth conconvolution(SPD-Conv)and multidimensional collaborative attention(MCA)mechanism to enhance the model's ability to extract features from low-resolution images and to suppress background interference,thus the attention to small foreign objects is increased.Secondly,the output part of the efficient layer aggregation network(ELAN)module is improved by using ghost convolution(Ghost-Conv)to significantly reduce the model's computational complexity.Finally,based on the scalable intersection over union(SIoU)optimized loss function,the model's training speed and robustness are further improved.Experimental results show that the proposed model achieves a mean aver-age precision(mAP)of 95.98%on the power transmission line foreign object detection dataset,which is higher than other mainstream comparative models.At the same time,the frames per second(FPS)reaches 64 to meet the real-time detection require-ments of foreign objects of power transmission lines.关键词
输电线路异物/YOLOv7/多维协作注意力/小目标/SPD/幻影卷积Key words
foreign object of transmission line/YOLOv7/MCA/small object/SPD/Ghost-Conv分类
动力与电气工程引用本文复制引用
严宇平,杨秋勇,谢翰阳,史建勋,邓琨,温启良..基于改进YOLOv7的输电线路异物检测模型[J].南方电网技术,2024,18(9):47-58,12.基金项目
国家自然科学基金资助项目(51977210) （51977210）
广东电网有限责任公司2020年信息中心个性化运营管控建设(生产监控指挥中心优化子项)(037800HK42200016).Supported by the National Natural Science Foundation of China(51977210) （生产监控指挥中心优化子项）
the 2020 Personalized Operation and Control Construction of Information Center of Guangdong Power Grid Co.,Ltd.,(Production Monitoring and Command Center Optimization Sub-Project)(037800HK42200016). （Production Monitoring and Command Center Optimization Sub-Project）
