LLTH‑YOLOv5:A Real‑Time Traffic Sign Detection Algorithm for Low‑Light Scenes  

作　　者：Xiaoqiang Sun Kuankuan Liu Long Chen Yingfeng Cai Hai Wang 

机构地区：[1]Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 212013,China [2]School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013,China

出　　处：《Automotive Innovation》2024年第1期121-137,共17页汽车创新工程（英文）

基　　金：National Natural Science Foundation of China,U20A20331,Long Chen.

摘　　要：Traffic sign detection is a crucial task for autonomous driving systems.However,the performance of deep learning-based algorithms for traffic sign detection is highly affected by the illumination conditions of scenarios.While existing algo-rithms demonstrate high accuracy in well-lit environments,they suffer from low accuracy in low-light scenarios.This paper proposes an end-to-end framework,LLTH-YOLOv5,specifically tailored for traffic sign detection in low-light scenarios,which enhances the input images to improve the detection performance.The proposed framework comproses two stages:the low-light enhancement stage and the object detection stage.In the low-light enhancement stage,a lightweight low-light enhancement network is designed,which uses multiple non-reference loss functions for parameter learning,and enhances the image by pixel-level adjustment of the input image with high-order curves.In the object detection stage,BIFPN is introduced to replace the PANet of YOLOv5,while designing a transformer-based detection head to improve the accuracy of small target detection.Moreover,GhostDarkNet53 is utilized based on Ghost module to replace the backbone network of YOLOv5,thereby improving the real-time performance of the model.The experimental results show that the proposed method significantly improves the accuracy of traffic sign detection in low-light scenarios,while satisfying the real-time requirements of autonomous driving.

关 键 词：Deep learning Traffic sign detection Low-light enhancement YOLOv5 Object detection 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]