北京交通大学学报2024，Vol.48Issue(5)：49-58,10.DOI:10.11860/j.issn.1673-0291.20230157
基于特征降冗余的Vanilla-YOLOv8铁路异物侵限检测方法
Vanilla-YOLOv8 railway foreign object intrusion detection method based on feature redundancy reduction
摘要
Abstract
Online monitoring technology for railway foreign object intrusion plays a critical role in ensur-ing the safety of railway operations and the security of passengers'lives and property.To address the issue of incomplete and inaccurate detection of occluded targets and small targets in existing foreign ob-ject detection algorithms,this paper introduces a railway foreign object detection algorithm,Vanilla-YOLOv8,based on YOLOv8.First,leveraging VanillaNet's approach of reducing network depth,shortcut branches,and enhancing nonlinear capabilities through deep training strategy modifications and dynamic adjustment of activation function states,the proposed method mitigates problems like model degradation,time inefficiency,and the disappearance of low-level small target features caused by excessive network layers and shortcut branches.This enhances the model's feature extraction capa-bility and detection speed.Then,improved partial convolution is employed to reduce redundant fea-tures,ensuring optimal utilization of extracted features.Finally,a Squeeze-and-Excitation(SE)atten-tion mechanism is integrated into the network backbone to increase the weight of key features,enhanc-ing feature representation and detection capabilities for occluded and small targets.Experimental re-sults show that the Vanilla-YOLOv8 algorithm achieves the mean average precision of 98.7%,re-duces parameters by 61.39%,and reaches the recognition speed of 125 Frames Per Second(FPS).These improvements mark a substantial advancement over traditional image processing techniques in terms of speed and detection accuracy,offering a valuable reference for real-time online monitoring.关键词
异物侵限检测/YOLOv8/特征冗余/VanillaNetKey words
foreign object intrusion detection/YOLOv8/feature redundancy/VanillaNet分类
交通工程引用本文复制引用
杜开华,许贵阳,白堂博..基于特征降冗余的Vanilla-YOLOv8铁路异物侵限检测方法[J].北京交通大学学报,2024,48(5):49-58,10.基金项目
国家自然科学基金(51975038) （51975038）
北京市自然科学基金(L211007,L221027) National Natural Science Foundation of China(51975038) （L211007,L221027）
Beijing Municipal Natural Science Foundation(L211007,L221027) （L211007,L221027）
