摘要
架空输电线路上的鸟巢侵占会对铁塔上的电力设备造成安全隐患,间接可能影响整个电力系统的稳定运行。针对目前架空输电线路鸟巢检测模型在复杂场景以及小目标场景下检测精度不高,检测效率低,模型复杂等问题。本研究提出一种基于YOLOv5s框架的轻量化架空输电线路鸟巢检测网络。首先在主干部分采用Fasternet重构YOLOv5s特征提取网络,降低模型复杂度,提高运行速度;然后在特征融合网络部分嵌入ConvMixer层,ConvMixer层的结构设计有助于在特征信息中更好的捕捉空间和通道的关系,提升模型对于小目标的检测能力;最后在特征融合网络部分引入ODConv模块,令送入检测头的特征图包含更多有效特征,提高模型对复杂场景和小目标的检测性能。实验结果表明,本文与基线模型YOLOv5s相比,计算量和模型体积分别减少了86%和72%,平均精度均值达到96.4%,检测速度达到104.2帧/s,验证了本文改进模型的有效性和可行性。
Bird nest encroachment on overhead transmission lines can cause safety hazards to the power equipment on the towers,which may indirectly affect the stable operation of the whole power system.Aiming at the current overhead transmission line bird′s nest detection model in the complex scene as well as the small target scene detection accuracy is not high,the detection efficiency is low,the model is complex and other problems.This study proposes a lightweight overhead transmission line bird′s nest detection network based on YOLOv5s framework.Firstly,the YOLOv5s feature extraction network is reconstructed by Fasternet in the backbone part,which reduces the model complexity and improves the operation speed;then the ConvMixer layer is embedded in the feature fusion network part,and the structural design of the ConvMixer layer helps to better capture the relationship between space and channel in the feature information,which improves the model′s detection ability for small targets;finally,the Finally,the ODConv module is introduced in the feature fusion network part,so that the feature map sent to the detection head contains more effective features to improve the detection performance of the model for complex scenes and small targets.The experimental results show that compared with the baseline model YOLOv5s,the computational amount and model volume are reduced by 86%and 72%,the average accuracy reaches 96.4%,and the detection speed reaches 104.2 frames/s,which verifies the effectiveness and feasibility of the improved model in this paper.
作者
徐业东
蔡亚恒
李严
刘学雷
曹英丽
Xu Yedong;Cai Yaheng;Li Yan;Liu Xuelei;Cao Yingli(College of Information and Electrical Engineering,Shenyang Agricultural University,Shenyang 110161,China;Agricultural Informatization EngineeringTechnology Center of Liaoning Province,Shenyang 110161,China)
出处
《电子测量技术》
北大核心
2024年第7期138-148,共11页
Electronic Measurement Technology
基金
国家自然科学基金(61903264)项目资助。
