华南理工大学学报（自然科学版）2025，Vol.53Issue(2)：48-57,10.DOI:10.12141/j.issn.1000-565X.240225
基于改进的CycleGAN和YOLOv8联合雾天道路环境感知算法
Foggy Road Environment Perception Algorithm Based on an Improved CycleGAN and YOLOv8
摘要
Abstract
In response to the issue of reduced road environment perception accuracy for intelligent vehicles under extreme haze conditions,this paper proposed a joint haze environment perception algorithm based on an improved CycleGAN and YOLOv8.Firstly,the CycleGAN algorithm was used as the framework for image defogging prepro-cessing.A self-attention mechanism was incorporated into the generator network to enhance the network's feature extraction capability.Additionally,to minimize color discrepancies with real images,a self-regularized color loss function was introduced.Secondly,in the object detection phase,the lightweight GhostConv network was first used to replace the original backbone network,reducing computational complexity.Furthermore,the GAM attention mechanism was added to the neck network to effectively improve the network's ability to interact with global infor-mation.Finally,the WIoU loss function was used to mitigate harmful gradients caused by low-quality samples,im-proving the model's convergence speed.Experiments conducted on the RESIDE and BDD100k datasets validate the proposed algorithm.Results show that the structural similarity between dehazed and original images is 85%.Compared to the original CycleGAN algorithm and the AODNet algorithm,the proposed approach improves the peak signal-to-noise ratio(PSNR)by 2.24 dB and 2.5 dB,respectively,and the structural similarity index(SSIM)by 15.4%and 36.3%,respectively.Additionally,the improved YOLOv8 algorithm demonstrates enhancements over the original algorithm,with precision,recall,and mean average precision(mAP)increasing by 2.5%,1.8%,and 1.1%,respectively.The experimental results confirm that the proposed algorithm outperforms traditional algo-rithms in terms of recall and detection accuracy,demonstrating its practical value关键词
智能车辆/环境感知/图像去雾/CycleGAN/目标检测/YOLOv8Key words
intelligent vehicle/environmental perception/image dehazing/CycleGAN/object detection/YOLOv8分类
信息技术与安全科学引用本文复制引用
岳永恒,雷文朋..基于改进的CycleGAN和YOLOv8联合雾天道路环境感知算法[J].华南理工大学学报（自然科学版）,2025,53(2):48-57,10.基金项目
国家自然科学基金项目(62173107) （62173107）
国家车辆事故深度调查体系项目(NAIS-ZL-ZHGL-2020018) （NAIS-ZL-ZHGL-2020018）
黑龙江省重点研发计划项目(JD22A014) Supported by the National Natural Science Foundation of China(62173107),the National Automobile Accident In-Depth Investigation System Funding Project(NAIS-ZL-ZHGL-2020018)and the Key R&D Program of Heilongjiang Province(JD22A014) （JD22A014）
