控制理论与应用2024，Vol.41Issue(5)：914-922,9.DOI:10.7641/CTA.2023.20475
可见光-红外特征交互与融合的YOLOv5目标检测算法
YOLOv5 object detection algorithm with visible-infrared feature interaction and fusion
摘要
Abstract
Object detection is the key technology of the autonomous driving system,but object detection algorithms based on RGB often perform poorly in scenarios such as nighttime and severe weather.Therefore,the object detection algorithms fusing visible and infrared information have begun to receive a lot of research attention.However,the existing methods usually have complex fusion structures and ignore the importance of information exchange between modalities.In this paper,we take YOLOv5 as the basic framework,and propose an object detection algorithm with visible-infrared feature interaction and fusion.It uses a new backbone network,CSPDarknet53-F,which uses a dual branch structure to extract visible and infrared features,respectively,and then reconstructs the information components and proportions of each mode through feature interaction modules to improve the information exchange between modalities so that visible and infrared features can be more fully integrated.Extensive experiments on the FLIR-aligned dataset and the M3FD dataset show that the CSPDarknet53-F used in our algorithm is more excellent in terms of synergistically utilizing visible and infrared information,which improves the detection accuracy of the model and has robustness against sudden changes in light intensity.关键词
可见光图像/红外图像/特征融合/交互/YOLOv5Key words
visible images/infrared images/feature fusion/interaction/YOLOv5引用本文复制引用
解宇敏,张浪文,余孝源,谢巍..可见光-红外特征交互与融合的YOLOv5目标检测算法[J].控制理论与应用,2024,41(5):914-922,9.基金项目
国家自然科学基金项目(61803161),广东省自然科学基金项目(2022A1515011887,2023A1515030119),清远市科技计划项目(2023DZX006),佛山市重点领域科技攻关项目(2020001006812),顺德区核心攻关项目(2030218000174),广州市科技计划项目(202102020379),江门市基础与应用基础研究项目(2020030103080008999)资助.Supported by the National Natural Science Foundation of China(61803161),the Natural Science Foundation of Guangdong Province(2022A1515011887,2023A1515030119),the Science and Technology Planning Project of Qingyuan(2023DZX006),the Key Area Research and Development Program of Foshan(2020001006812),the Shunde District Core Technology Research Project(2030218000174),the Science and Technology Plan Project of Guangzhou(202102020379)and the Science and Technology Plan Project of Jiangmen(2020030103080008999). （61803161）
