计算机工程与应用2025，Vol.61Issue(4)：262-271,10.DOI:10.3778/j.issn.1002-8331.2405-0103
PMM-YOLO:多尺度特征融合的交通标志检测算法
PMM-YOLO:Traffic Sign Detection Algorithm with Multi-Scale Feature Fusion
摘要
Abstract
Traffic signs play a crucial role in the field of autonomous driving.However,they often present challenges such as small size,susceptibility to occlusion,and missed detections and false alarms in complex environments.This paper proposes a PMM-YOLO traffic sign detection algorithm based on improvements to YOLOv5.To effectively extract multi-scale information and enhance the model's feature representation capability,an adaptive parallel atrous convolution(APA)module combining attention mechanism is introduced.Utilizing parallel atrous convolutions with different dilation rates enables effective extraction of features at various scales,while a gate mechanism highlights the representation of key targets,improving detection accuracy.A multi-branch adaptive sampling(MBAS)approach is designed to provide multiple feature extraction pathways for the network,enriching feature expression diversity.The important features are rein-forced by the weight at different positions,and redundant features are suppressed.A multi-scale feature fusion(MSFF)module is devised to concatenate feature maps of different sizes,leveraging multi-scale information to fuse feature maps of multiple scales comprehensively,thus obtaining more comprehensive target features and enhancing detection perfor-mance.An output reorganization(ORO)module is constructed to enhance the detection of small targets by adding a small target detection layer and removing the large target detection layer,thereby reducing model complexity accordingly.Experimental results demonstrate that the PMM-YOLO algorithm achieves an mAP@0.5 of 86.4%on the TT100K dataset,representing a 5.9 percentage points improvement over the original YOLOv5.Additionally,the FPS is increased by 4.4%compared to the baseline,enabling rapid and accurate detection of traffic signs.关键词
交通标志检测/YOLOv5/多分支采样/特征融合/空洞卷积/注意力机制Key words
traffic sign detection/YOLOv5/multi-branch sampling/feature fusion/atrous convolution/attention mechanism分类
信息技术与安全科学引用本文复制引用
赵磊,李栋..PMM-YOLO:多尺度特征融合的交通标志检测算法[J].计算机工程与应用,2025,61(4):262-271,10.基金项目
内蒙古自治区自然科学基金(2022QN06004). （2022QN06004）
