机电工程技术2024，Vol.53Issue(11)：211-214,4.DOI:10.3969/j.issn.1009-9492.2024.00085
基于改进YOLOv8的铁路异物侵限检测方法
A Railway Foreign Object Intrusion Detection Method Based on Improved YOLOv8
摘要
Abstract
Efficient and accurate detection of foreign objects invading railways from surveillance images is of great significance for ensuring the safety of train operation.Aiming at the problem that small targets and objects with high similarity to the background appearing during train operation are difficult to be recognized accurately,an improved YOLOv8 algorithm that can detect the encroachment limits in railroad images quickly and accurately is proposed.Firstly,the CBAM attention mechanism is introduced in the Backbone network backbone layer to improve the speed of track foreign object feature extraction,so that the model pays more attention to the key features in the image,while suppressing the irrelevant railroad background information.Secondly,to address the fuzzy definition of the CIoU loss function in the model in terms of the width-to-height ratio,the EIOU loss function is used to replace the original loss function,minimize the difference between the width and height of the target box and the anchor box,and improve the accuracy of the bounding box regression while accelerating the convergence of the model.Finally,the group convolution is used to optimize the traditional target detection head,which improves the efficiency of the model without losing the accuracy of the model,and makes the model have better performance in practical applications.The experimental results show that the improved YOLOv8 algorithm achieves a mAP value of 96.2%on the dataset,which is a high level of detection accuracy and proves that the model has value for application in real life.关键词
异物侵限检测/深度学习/目标检测/YOLOv8算法/CBAM注意力机制/EIoU/RepConvKey words
foreign object intrusion limit detection/deep learning/target detection/YOLOv8 algorithm/CBAM attention mechanism/EIoU/RepConv分类
交通工程引用本文复制引用
陈伟迅,柯旭能,孟思明..基于改进YOLOv8的铁路异物侵限检测方法[J].机电工程技术,2024,53(11):211-214,4.基金项目
广东省普通高校创新团队项目(自然科学)(2021KCXTD068) （自然科学）
