文献类型：期刊文献

英文题名：ES-YOLO: Edge and Shape Fusion-Based YOLO for Traffic Sign Detection

作者：Pan, Weiguo[1];Du, Songjie[2];Xu, Bingxin[1];Zhang, Bin[1];Liu, Hongzhe[1]

第一作者：潘卫国

通讯作者：Du, SJ[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China.

年份：2026

卷号：87

期号：1

外文期刊名：CMC-COMPUTERS MATERIALS & CONTINUA

收录：;EI(收录号：20260720049671);WOS:【SCI-EXPANDED(收录号:WOS:001696166400001)】；

基金：Funding Statement: This work was supported by the National Natural Science Foundation of China (Grant Nos. 62572057, 62272049, U24A20331) , Beijing Natural Science Foundation (Grant Nos. 4232026, 4242020) , Academic Research Projects of Beijing Union University (Grant No. ZK10202404) .

语种：英文

外文关键词：Traffic sign; edge information; shape prior; feature fusion; object detection

摘要：Traffic sign detection is a critical component of driving systems. Single-stage network-based traffic sign detection algorithms, renowned for their fast detection speeds and high accuracy, have become the dominant approach in current practices. However, in complex and dynamic traffic scenes, particularly with smaller traffic sign objects, challenges such as missed and false detections can lead to reduced overall detection accuracy. To address this issue, this paper proposes a detection algorithm that integrates edge and shape information. Recognizing that traffic signs have specific shapes and distinct edge contours, this paper introduces an edge feature extraction branch within the backbone network, enabling adaptive fusion with features of the same hierarchical level. Additionally, a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network, aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise. The algorithm was evaluated on the CCTSDB and TT100k datasets, and compared to YOLOv8s, the mAP50 values increased by 3.0% and 10.4%, respectively, demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.