计算机工程与应用2024，Vol.60Issue(5)：200-209,10.DOI:10.3778/j.issn.1002-8331.2306-0293
改进YOLOv5-S的交通标志检测算法
Traffic Sign Detection Algorithm Based on Improved YOLOv5-S
摘要
Abstract
In the field of autonomous driving,existing traffic sign detection methods have problems with missed or incor-rect sign detection in complex backgrounds,reducing the reliability of intelligent vehicles.To address this issue,a real-time traffic sign detection algorithm is proposed to enhance YOLOv5-S.Firstly,the coordinate attention mechanism is inte-grated into the feature extraction network to perceive the location of the object by establishing long-term dependencies on the target,making the algorithm focus on high-priority regions.Secondly,the Focal-EIoU loss function is used to replace the CIoU,allowing the network to focus more on high-quality classification samples,improving the network's ability to learn from difficult samples and reducing the occurrence of missed or false detections.Next,the lightweight convolution technique GSConv is integrated into the network to reduce the complexity of the model.Finally,a new small target detec-tion layer is added to improve the algorithm's detection of small-sized signs by using richer feature information.The experi-mental results show that the improves algorithm achieves 88.1% for mAP@0.5 and 68.5% for mAP@0.5:0.95,with a detection speed of 83 FPS,which can meet the requirements of real-time and reliable detection.关键词
交通标志检测/YOLOv5/坐标注意机制/Focal-EIoU/GSConvKey words
traffic sign detection/YOLOv5/coordinate attention mechanism/Focal-EIoU/GSConv分类
信息技术与安全科学引用本文复制引用
刘海斌,张友兵,周奎,张宇丰,吕圣..改进YOLOv5-S的交通标志检测算法[J].计算机工程与应用,2024,60(5):200-209,10.基金项目
湖北省科技重大专项(2020AAA001) （2020AAA001）
湖北省重点研发计划项目(2021BED004,2023BAB169). （2021BED004,2023BAB169）
