计算机工程与应用2024，Vol.60Issue(14)：209-218,10.DOI:10.3778/j.issn.1002-8331.2303-0127
改进SAF-FCOS的雷视融合目标检测算法
Improved SAF-FCOS Target Detection Algorithm Based on Radar-Vision Fusion
摘要
Abstract
An improved SAF-FCOS radar-vision fusion target detection network is proposed to address the difficulty in effectively utilizing radar point cloud information and image features,as well as the issue of false or missed detections in harsh weather environments.The backbone network structure of SAF-FCOS is improved,and multi-scale fusion of radar feature information is carried out at C3 and C4 feature layers,so that the network model can make full use of radar infor-mation.Using improved LNblock module-LNblcok_GAM before detection layers can extract image features at a lower computational cost while improving the detection performance of the network.In terms of regression loss,the improved CEIOU based on EIOU and GIOU is used to replace the GIOU in the original network,improving the detection accuracy of the network and enhancing the robustness of the model.On the NuScenes dataset,the improved network achieves 70.7％mAP0.5∶0.95 and 90.5％AP50,respectively,which are 1.7 percentage points and 0.9 percentage points higher than the original network SAF-FCOS.The missed and false detections are effectively reduced,and the overall detection perfor-mance of the improved network is better than other classic visual target detection algorithms.关键词
目标检测/雷视融合/SAF-FCOS网络/多尺度融合/LNblock_GAMKey words
target detection/radar-vision fusion/SAF-FCOS network/multi-scale fusion/LNblock_GAM分类
信息技术与安全科学引用本文复制引用
陈正浩,邓月明,谢竞,何鑫..改进SAF-FCOS的雷视融合目标检测算法[J].计算机工程与应用,2024,60(14):209-218,10.基金项目
国家自然科学基金(62173140,62072175) （62173140,62072175）
湖南省重点研发计划项目(2022GK2067) （2022GK2067）
湖南省自然科学基金(2021JJ30452) （2021JJ30452）
湖南省教育厅科学研究项目(21C0008). （21C0008）
