沈阳航空航天大学学报2024，Vol.41Issue(2)：68-75,8.DOI:10.3969/j.issn.2095-1248.2024.02.008
基于深度学习的输电线路目标检测
Transmission line target detection based on improved deep learning
摘要
Abstract
Aiming at the current target detection methods based deep learning for transmission line,the feature extraction ability is poor for small target,easy to misdetection leakage detection,detection accu-racy is low,detection speed is slow.A transmission line target detection method was proposed based on an improved neural network model YOLOv7.Firstly,the MobileNetV2 network was used as the feature extraction part of YOLOv7 to achieve lightweight processing of the model.Secondly,the CA mecha-nism and ASPP module were introduced to improve the accuracy and perception of the model.Finally,the self-drawn transmission line obstacle data set was used for training.Improved YOLOv7 network an-dare compared with the original YOLOv7 model.The results show that the algorithm proposed has sig-nificantly improved the accuracy and recall rate,which meets the fault detection in complex scenarios and is more conducive to model deployment of mobile devices and embedded systems.关键词
输电线路/目标检测/神经网络/轻量化模型/注意力机制/深度学习Key words
transmission line/target detection/neural network/lightweight model/attention mecha-nism/deep learning分类
信息技术与安全科学引用本文复制引用
刘艳梅,陈鑫顺,陈震,孙改生..基于深度学习的输电线路目标检测[J].沈阳航空航天大学学报,2024,41(2):68-75,8.基金项目
教育部春晖计划(项目编号:HZKY20220431),国网辽宁省电力有限公司科技项目(项目编号:SGLNYJ00QXJS2200005) （项目编号:HZKY20220431）
