基于改进CenterNet的输电线路异物目标检测  

FOREIGN BODIES DETECTING IN HIGH-VOLTAGE TRANSMISSION LINE BASED ON IMPROVED CENTERNET

作　　者：王明[1] 宋公飞[1,2,3] 王瑞绅 张子梦 Wang Ming;Song Gongfei;Wang Ruishen;Zhang Zimeng(School of Automation,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;Key Laboratory of Advanced Control and Optimization for Chemical Processes,Shanghai 200237,China;Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing 210044,Jiangsu,China)

机构地区：[1]南京信息工程大学自动化学院,江苏南京210044 [2]化工过程先进控制和优化技术教育部重点实验室,上海200237 [3]江苏省大气环境与装备技术协同创新中心,江苏南京210044

出　　处：《计算机应用与软件》2025年第4期129-134,共6页Computer Applications and Software

基　　金：国家自然科学基金面上项目(61973170);中央高校基本科研业务费专项资金项目(2020ACOCP02)。

摘　　要：针对高压输电线路会存在异物影响线路正常运行,提出一种改进CenterNet的高压输电线路巡检异物的检测方法,对常见的输电线路异物小目标进行检测。该方法基于空洞卷积设计宽型残差模块,加宽残差模块的特征提取面,并且分别采用通道注意力机制与空间注意力机制对特征信息进行二次处理,优化检测精度。实验结果显示,改进后的平均精度从88.59%提升到93.09%,整体提升了4.5百分点。For high-voltage transmission lines,there will be foreign bodies affecting the normal operation of the line.This paper proposes an improved CenterNet method for detecting foreign bodies in high-voltage transmission line inspection to detect common small targets of foreign bodies in transmission lines.In this method,a wide residual module was designed based on cavity convolution,and the feature extraction surface of the residual module was widened.The channel attention mechanism and spatial attention mechanism were used to process the feature information twice,and the detection accuracy was optimized.The experimental results show that the improved average accuracy is improved from 88.59%to 93.09%,and the overall accuracy is improved by 4.5 percentage points.

关 键 词：深度学习 目标检测 CenterNet 输电线路 残差网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]