YOLO-based lightweight traffic sign detection algorithm and mobile deployment  

作　　者：WU Yaqin ZHANG Tao NIU Jianjun CHANG Yan LIU Ganjun 

机构地区：[1]Software College,Shanxi Agricultural University,Taigu 030800,China [2]School of Electrical Automation and Information Engineering,Tianjin University,Tianjin 300072,China

出　　处：《Optoelectronics Letters》2025年第4期249-256,共8页光电子快报(英文版)

基　　金：supported by the Shanxi Agricultural University Science and Technology Innovation Enhancement Project。

摘　　要：This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).

关 键 词：c f layer simple attention module simam reduce complexity traffic sign detection prioritize key features backbone networkemploying classification backbone networknextthe 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]