现代电子技术2025，Vol.48Issue(11)：174-179,6.DOI:10.16652/j.issn.1004-373x.2025.11.027
基于改进YOLOv8的轨道小尺度异物入侵算法研究
Improved YOLOv8 based algorithm for small-scale foreign object intrusion detection on railways
摘要
Abstract
An improved YOLOv8-SGFE track intrusion detection model is proposed to improve the detection accuracy for small objects,and reduce the large model size and the high deployment costs in the current train track obstacle detection methods.To reduce the network's computational load,an SGConv module is designed based on the small object detection module SPD-Conv,and the SGConv module is used to replace the standard convolutional layers in the backbone of YOLOv8.To enhance the model's perceptual capability,the efficient multi-scale attention(EMA)is combined with the C2f-Faster module to form the C2f-Faster-EMA module.The C2f-Faster-EMA module is used to replace the C2f module in YOLOv8.The improved YOLOv8-SGFE model is applied to a custom railway track intrusion dataset.In comparison with the YOLOv8 model,the proposed model's parameters are decreased by 36.04%,and its FLOPs are reduced from 28.7×109 to 19×109.Despite the significant reduction in computational load,its mAP is increased by 2.5%.Experimental results demonstrate that the proposed algorithm achieves higher detection accuracy,with reduced model parameters and computational load,so it is suitable for detecting track obstacles in complex environments and easier to be deployed on mobile devices.关键词
轨道异物入侵/小目标检测/部分卷积/高效多尺度注意力/YOLOv8/轻量化Key words
track foreign object intrusion/small object detection/partial convolution/EMA/YOLOv8/lightweight分类
信息技术与安全科学引用本文复制引用
冯庆胜,付明雨,姚泽圆,刘杨,梁天添..基于改进YOLOv8的轨道小尺度异物入侵算法研究[J].现代电子技术,2025,48(11):174-179,6.基金项目
辽宁省教育厅科学研究项目(JYTMS20230008) （JYTMS20230008）
辽宁省教育厅基本科研项目(JYTMS20230037) （JYTMS20230037）
