摘要
针对地铁低照度环境中侵限物体检测困难的问题,提出了一种基于改进YOLO(You Only Look Once)v8的侵限物体识别方法,通过集成Retinexformer低照度增强网络和大规模内核注意力(LSKA,Large Scale Kernel Attention)模块,显著改善了低对比度目标的特征提取能力,在保持轻量化的同时提升了复杂场景下的检测性能。在自行构建的地铁低照度侵限物体数据集上进行实验验证,结果表明:改进YOLOv8后得到的Retinexformer-LSKA-YOLOv8n模型在mAP50-95指标上达到0.839,相比原始YOLOv8n模型提升约9.24%,较传统Faster R-CNN模型提升32.19%。该模型在识别性能上有较为显著的提升,能够较为准确地检测地铁低照度场景下的侵限物体,为地铁安全运营提供技术支持。
This paper proposed a target detection method based on improved YOLO(You OnlyLook Once)v8 to address the difficulty of detecting intrusive objects in metro low-illumination environments.This method significantly improved the feature extraction ability of low contrast targets by integrating RetinexFormer low light enhancement network and LSKA(Large Scale Kernel Attention)module,while maintaining lightweight and enhancing detection performance in complex scenes.The paper conducted experimental verification on a self-constructed subway low light intrusion object dataset,and the results showed that the improved Retinexformer LSKA-YOLOv8n model achieved 0.839 on the mAP50-95 index,which was about 9.24%higher than the original YOLOv8n model and 32.19%higher than the traditional Faster R-CNN model.This model has significantly improved recognition performance and can accurately detect intrusion objects in low light scenes of the subway,provide technical support for the safe operation of the metro.
作者
彭凯贝
邹健贤
吕晓军
PENG Kaibei;ZOU Jianxian;LYU Xiaojun(Institute of Computing Technologies,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China)
出处
《铁路计算机应用》
2025年第9期1-5,共5页
Railway Computer Application
基金
中国铁道科学研究院集团有限公司科研项目(2023YJ088)。
