软件导刊2024，Vol.23Issue(12)：249-254,6.DOI:10.11907/rjdk.232297
改进YOLOV5的密集行人检测算法研究
Research on Improved YOLOV5 Algorithm for Dense Pedestrian Detection
摘要
Abstract
A feature fusion algorithm FPCA-YOLOV5 with improved YOLOV5 is proposed to address the issues of missed detection of dense pedestrians and low detection accuracy.Firstly,by combining the spatial pooling pyramid structure SPPFCSPC with CA attention,the model has stronger expressive power.Secondly,adding PP modules to the network and changing the detection layer from three to four layers can achieve more accurate detection of small targets.Finally,a novel downsampling mechanism,CAConv,was designed to enable the network to focus more on important channels when processing feature maps.The experimental results show that on the public dataset WiderPerson,the improved YOLOV5 model has increased recall by 3.4%and average accuracy by 2.3%compared to the original model.The overall perfor-mance is significantly improved compared to the original model,demonstrating the effectiveness of the FPCA-YOLOV5 algorithm in object de-tection.关键词
YOLOV5/行人检测/特征融合/注意力机制/小目标检测Key words
YOLOV5/pedestrian detection/feature fusion/attention mechanism/small object detection分类
信息技术与安全科学引用本文复制引用
周龙刚,魏本昌,魏鸿奥,张路,刘洋..改进YOLOV5的密集行人检测算法研究[J].软件导刊,2024,23(12):249-254,6.基金项目
湖北省教育厅项目(B2019077) （B2019077）
湖北汽车工业学院博士科研启动基金项目(BK201603) （BK201603）
