融合混合注意力和改进YoloX的铁路落石检测方法

A railway rockfall detection method incorporating mixed attention and improved YoloX

铁路周边危岩落石侵入铁路限界内会严重危害旅客生命财产与铁路行车安全。针对传统检测方法对复杂动态环境识别误检高、小目标识别准确率较低等问题,提出一种基于视频的铁路落石侵限检测深度学习方法。首先,融合混合注意力模块,增强网络对与背景相似落石检测能力。其次,改进YoloX部分网络结构为双向特征金字塔网络,加强了不同层级特征的相互交流,提升小目标识别能力。同时采集大量不同场景模拟落石数据,构建模拟落石数据集,并在训练中使用Mosaic数据增强方法,增强方法的泛化能力。实验结果表明,本文方法随着改进模块的添加,识别精度不断提高。对比多种主流目标检测方法,取得了最高识别准确度,不同大小目标识别稳定,证明了本文算法在实际铁路场景的应用价值。

Dangerous rocks and falling rocks around the railway intrude into the railway boundary, which will seriously endanger the life and property of passengers and the safety of railway traffic Aiming at the problems that traditional detection methods have high false detection in complex dynamic environment and low accuracy of small target recognition, a video-based deep learning method for railway rockfall intrusion detection is proposed. First, a hybrid attention module is incorporated into the network structure, which can enhance the network′s ability to detect rockfalls similar to the background. Secondly, part of the network structure of YoloX is improved to a bidirectional feature pyramid network, which strengthens the mutual exchange of features at different levels and improves the ability to identify small targets. Simultaneously collect a large number of simulated rockfall data from different scenarios, build a simulated rockfall data set, and use the Mosaic data enhancement method in training to enhance the generalization ability of the method. The experimental results show that with the addition of improved modules, the identification accuracy of the method in this paper is continuously improved. Compared with various mainstream target detection methods, the highest identification accuracy is achieved, and the identification of different sizes targets is stable, which proves the application value of the algorithm in this paper in the actual railway scene.