雷达科学与技术2025，Vol.23Issue(1)：82-91,100,11.DOI:10.3969/j.issn.1672-2337.2025.01.009
基于轻量化卷积神经网络车载雷达图像目标识别方法
Object Recognition Method for Automotive Radar Images Based on Lightweight Convolutional Neural Network
摘要
Abstract
To address the issues of blurry details and small target proportions in automotive millimeter-wave radar images,as well as the complexity of convolutional neural network models that are difficult to deploy on the edge,an auto-motive radar image target recognition method based on lightweight convolutional neural network YOLOv5s is proposed.First,a lightweight decoupled head is designed by incorporating Ghost convolution,enabling parallel processing of detection and classification tasks.Next,the Concat_att module enhanced with attention mechanism is designed,and a more boundary-sensitive network loss function EIoU Loss is introduced to fully extract detailed information of small objects in feature maps,accelerating network convergence and improving accuracy.Finally,Slim pruning is applied to further compress the storage space of the model and reduce computational complexity.The experimental results indicate that when the model size is reduced to 76.8%of the original YOLOv5s network,the mAP@0.5 and mAP@0.5:0.95 are respectively improved by 2.7%and 2.8%compared to the baseline network.This method is suitable for small target detection and meets both the precision and real-time requirements of target recognition,making it appropriate for deploy-ment in automotive embedded systems.关键词
雷达图像/YOLOv5s/轻量化/注意力机制/模型剪枝Key words
radar images/YOLOv5s/lightweight/attention mechanism/model pruning分类
电子信息工程引用本文复制引用
李家强,汪星宇,陈金立,姚昌华..基于轻量化卷积神经网络车载雷达图像目标识别方法[J].雷达科学与技术,2025,23(1):82-91,100,11.基金项目
国家自然科学基金(No.62071238) （No.62071238）
