摘要
Abstract
To tackle degraded vehicle detection performance caused by hardware constraints,multi-scale objects,and occlusions in autonomous driving,this paper proposes RT-DETR-light,a lightweight detection algorithm.First,we design a CG Block to enhance the backbone network,forming the lightweight feature extractor CGResNet,which balances speed and accuracy.A bidirectional feature pyramid network BiFPN is then introduced for feature fusion to improve precision via bidirectional information flow.Furthermore,an enhanced loss function,EPGIoU,is proposed to improve localization accuracy for small and occluded vehicles by stabilizing gradient optimization via multi-constraint collaboration.Experiments on the UA-DETRAC dataset show a mAP@0.5 of 75.0%and a precision of 74.5%.Compared to the baseline,it reduces parameters and computation by 26.4%and 18.0%,respectively,while improving detection speed by 1.4 percentage points.Cross-dataset evaluation on BDD100K-Sub confirms its strong generalization ability.The proposed algorithm offers superior accuracy,lightweight design,and inference speed,providing an effective solution for real-time vehicle detection and edge device deployment.关键词
深度学习/RT-DETR算法/轻量化/车辆目标检测Key words
deep learning/RT-DETR algorithm/lightweight/vehicle object detection分类
信息技术与安全科学
