计算机工程与科学2026，Vol.48Issue(3)：540-550,11.DOI:10.3969/j.issn.1007-130X.2026.03.016
基于改进YOLOv8的低光行人检测算法
An improved low-light pedestrian detection algorithm based on YOLOv8
摘要
Abstract
In order to solve the problem that the current mainstream low-light pedestrian detection framework has poor performance due to insufficient image brightness and contrast in this task,this pa-per proposes the RetinaHA-YOLOv8 algorithm.The algorithm uses RetinexFormer as a pre-processing module to restore the damaged image,ensuring that the subsequent algorithm can extract clearer and more useful features from the enhanced image.Additionally,it uses the hybrid attention transformation(HAT)attention mechanism to retain key information in the initial stage and promote deep fusion after feature fusion.Finally,in order to balance the additional computational burden and meet the real-time detection requirements,the online re-parameterized convolution technology is introduced to improve the inference speed and frames per second while maintaining the detection accuracy.The experimental results verify the effectiveness of the RetinaHA-YOLOv8 algorithm on the public low-light pedestrian detection dataset,with AP increased by 5.4%,11.7%and 9.5%respectively,while meeting the real-time requirements in practical applications.关键词
低光行人检测/RetinexFormer框架/HAT注意力机制/在线重参数化卷积Key words
low-light pedestrian detection/RetinexFormer framework/HAT attention mechanism/online reparameterized convolution分类
信息技术与安全科学引用本文复制引用
徐广平,徐慧英,朱信忠,黄晓,王舒梦,宋杰..基于改进YOLOv8的低光行人检测算法[J].计算机工程与科学,2026,48(3):540-550,11.基金项目
国家自然科学基金(62376252) （62376252）
浙江省自然科学基金(LZ22F030003) （LZ22F030003）
