现代电子技术2025，Vol.48Issue(9)：143-153,11.DOI:10.16652/j.issn.1004-373x.2025.09.022
面向复杂场景的多尺度行人和车辆检测算法
Multi-scale pedestrian and vehicle detection algorithm for complex scenes
王娟敏 1皮建勇 1黄昆 1胡伟超 1胡倩1
作者信息
- 1. 贵州大学 计算机科学与技术学院 公共大数据国家重点实验室,贵州 贵阳 550000
- 折叠
摘要
Abstract
A YOLOv8-based improved detection algorithm named RDRFM-YOLO is presented to address the issue of missed detections caused by multi-scale cases and occlusion in pedestrian and vehicle detection tasks.For the backbone network,the RFDRep module is designed to replace the convolution and C2f modules,enhancing the network's capability to capture features at different scales.For the neck network,the SFMS module is designed for optimization,improving the model's ability to extract features of occluded objects.Experiments on a custom pedestrian and vehicle dataset show that the algorithm RDRFM-YOLO outperforms the original algorithm,maintaining high detection efficiency.The mAP@0.5 of the RDRFM-YOLO reaches 56.7%,and its mAP@0.5:0.95 reaches 37.3%,which are improvements of 2.8%and 2.3%,respectively,over the original algorithm.Its parameter count and floating-point operations are 3.3×106 and 9.2×109,only increasing by 0.1×106 and 0.3×109,respectively,in comparison with those of the original algorithm.Additionally,the model shows good performance across multiple datasets.关键词
行人和车辆检测/多尺度/遮挡/RDRFM-YOLO/RFDRep模块/SFMS模块Key words
pedestrian and vehicle detection/multi-scale/occlusion/RDRFM-YOLO/RFDRep module/SFMS module分类
电子信息工程引用本文复制引用
王娟敏,皮建勇,黄昆,胡伟超,胡倩..面向复杂场景的多尺度行人和车辆检测算法[J].现代电子技术,2025,48(9):143-153,11.
