AHLNet:Adaptive Multihead Structure and Lightweight Feature Pyramid Network for Detection of Live Working in Substations

作     者：Mengle Peng Xiaoyong Jiang Langyue Huang Zhongyi Li Haiteng Wu Xiaotang Geng 

作者机构：Zhejiang University of Science and TechnologyHangzhou310023China Zhejiang Key Laboratory of Intelligent Operation and Maintenance RobotHangzhou311100China 

出 版 物：《Machine Intelligence Research》 (机器智能研究（英文版）)

年 卷 期：2024年第21卷第5期

页      面：983-992页

核心收录：

学科分类：08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the General Scientific Research Project of the Education Department of Zhejiang Province China(No.Y202146060) 

主　　题：Adaptive multihead structure lightweight feature pyramid substation feature imbalance multiobject detection 

摘      要：With the increasing demand for power in society,there is much live equipment in substations,and the safety and standardization of live working of workers are facing *** at these problems of scene complexity and object diversity in the real-time detection of the live working safety of substation workers,an adaptive multihead structure and lightweight feature pyramid-based network(AHLNet)is proposed in this study,which is based on ***,we take AH-Darknet53 as the backbone network of YOLOV3,which can introduce an adaptive multihead(AMH)structure,reduce the number of network parameters,and improve the feature extraction ability of the backbone ***,to reduce the number of convolution layers of the deeper feature map,a lightweight feature pyramid network(LFPN)is proposed,which can perform feature fusion in advance to alleviate the problem of feature imbalance and gradient ***,the proposed AHLNet is evaluated on the datasets of 16 categories of substation safety operation scenarios,and the average prediction accuracy MAP_(50)reaches 82.10%.Compared with YOLOV3,MAP_(50)is increased by 2.43%,and the number of parameters is 90 M,which is only 38%of the number of parameters of *** addition,the detection speed is basically the same as that of YOLOV3,which can meet the real-time and accurate detection requirements for the safe operation of substation staff.