空间定位与特征泛化增强的铁路异物跟踪检测
Railway foreign objects tracking detection based on spatial location and feature generalization enhancement
陈永 1王镇 2周方春2
作者信息
- 1. 兰州交通大学电子与信息工程学院,兰州 730070;甘肃省人工智能与图形图像处理工程研究中心,兰州 730070
- 2. 兰州交通大学电子与信息工程学院,兰州 730070
- 折叠
摘要
针对现有深度学习异物跟踪检测算法易受复杂环境、目标遮挡等影响,导致出现漏检及检测精度低等问题,提出了一种空间定位与特征泛化增强的铁路异物跟踪检测算法.提出改进多尺度级联GhostNet特征提取网络,提升对红外目标的特征提取能力;利用异物空间位置定位与泛化形态信息,设计空间定位与特征泛化增强模块,增强对复杂场景下位置移动与跟踪轨迹变化目标的检测精度;构建金字塔预测网络,得到红外铁路异物的检测锚框、类别及置信度信息;通过改进类别和置信度显示的DeepSORT跟踪算法,结合卡尔曼滤波与匈牙利算法实现红外弱光环境下铁路异物跟踪检测.实验结果表明:所提算法对铁路异物的跟踪检测精确度达到 83.3%,平均检测速度为 11.3帧/s;与比较算法相比,所提算法检测精度更高,对红外弱光场景下铁路异物跟踪检测具有较好的性能.
Abstract
There are factors of complex environments,target occlusion,and others.These factors lead to the lack of detection and low detection accuracy of existing depth learning foreign object tracking algorithms.A railway foreign object tracking technique based on spatial location and feature generalization enhancement is proposed to address the issues with the current deep learning video tracking system.The multi-scale cascaded GhostNet network is used to improve the feature extraction ability of the model.The infrared features are enhanced by spatial location and feature generalization module.The module combined with infrared foreign object spatial location and generalization morphology.The detection accuracy of the network is enhanced.The detection anchor size,target kind,and confidence of infrared railway foreign materials are obtained by using the pyramid prediction network.The DeepSORT tracking algorithm which improved category and confidence combined with Kalman filtering and the Hungarian algorithm is used to track railway foreign objects in an infrared weak light environment.The experimental results show that the tracking precision of the proposed algorithm for infrared targets reaches 83.3%,and the average detection rate of the proposed method is 11.3 frames per second.Compared with the comparison method,the proposed algorithm has good performance for tracking railway foreign objects in infrared weak light scenes.
关键词
机器视觉/异物检测/红外弱光/空间定位/特征泛化增强/目标跟踪Key words
machine vision/foreign object detection/infrared weak light/spatial location/feature generalization enhancement/target tracking引用本文复制引用
出版年
2025
国家科技期刊平台
NSTL
维普
万方数据