计算机工程2025，Vol.51Issue(5)：361-369,9.DOI:10.19678/j.issn.1000-3428.0069025
基于改进YOLOv5s的被遮挡交通标志检测算法
Blocked Traffic Sign Detection Algorithm Based on Improved YOLOv5s
摘要
Abstract
The detection and recognition of road traffic signs are extremely important in intelligent driving.When traffic signs are obstructed,problems such as small targets and low detection accuracy occur.This paper proposes an improved model for obstructed traffic sign recognition based on YOLOv5s.To address the issue of a lack of dataset for traffic signs under occlusion,a self-made Chinese Occlusion Traffic Sign Dataset(COTSD)is developed.A backbone network based on MobileNetv2 improvement is proposed to extract rich discriminative features for fine-grained object detection.For small and obstructed traffic signs with low resolution,ordinary linear interpolation methods cannot capture higher-order and more detailed features;therefore,an improved Dynamic weight Upsampling Module(DEUM)is designed to integrate channel information weighted by channel attention for pixel rearrangement and generate high-resolution images.To address the sensitivity of loss functions such as CIoU(Complete IoU)to small target position changes,Normalized Gaussian Wasserstein Distance(NWD)is used to optimize the bounding box regression loss.For the self-made occluded COTSD dataset,the accuracy is 93.60％,recall is 72.50％,F1 value is 81.71％,and mAP@0.5 is 79％.For the publicly available CCTSDB dataset containing a small number of occluded traffic signs,the accuracy is 92.2％,recall is 78.8％,F1 value is 85％,and mAP@0.5 is 88.5％.The experimental results for the two datasets demonstrate that the improved algorithm can effectively improve the detection accuracy of traffic signs after occlusion.关键词
交通标志检测/遮挡条件/MobileNetv2模型/动态权重上采样模块/归一化高斯Wassertein距离Key words
traffic sign detection/occlusion conditions/MobileNetv2 model/Dynamic weight Upsampling Module(DEUM)/Normalized Gaussian Wasserstein Distance(NWD)分类
信息技术与安全科学引用本文复制引用
蓝章礼,邢彩卓,张洪..基于改进YOLOv5s的被遮挡交通标志检测算法[J].计算机工程,2025,51(5):361-369,9.基金项目
国家自然科学基金(52278291) （52278291）
重庆交通大学研究生科研创新项目(2024s0105). （2024s0105）
