School of Railway Transport, Shaanxi Railway Institute, Weinan 714000, Shaanxi, China.
School of Urban Rail Engineering, Shaanxi Railway Institute, Weinan 714000, Shaanxi, China.
Comput Intell Neurosci. 2022 Jun 30;2022:1920196. doi: 10.1155/2022/1920196. eCollection 2022.
With the full popularity of China's railwayization process, it has brought about the problem of the management ability of railway traffic safety. Railway traffic safety emergency management capabilities are low. When an accident occurs, clearer data cannot be obtained in the first time to have a general understanding of the accident. Therefore, the problem of organizing rescue has always plagued relevant railway workers. This study aims to study the improvement of railway traffic emergency management based on image recognition technology in the context of big data. To this end, this study proposes image recognition technology based on deep learning, and through the relayout of the railway traffic emergency management system, so that the railway traffic problems can be dealt within time as soon as they occur, and designed an experiment to explore the ability of image recognition. The results of the experiment show that the efficiency of the improved railway traffic emergency management system has increased by 27%, and the recognition capability has increased by 64%. It can very well help current railway workers to carry out emergency management for railway traffic safety.
随着中国铁路化进程的全面普及，带来了铁路交通安全管理能力的问题。铁路交通安全应急管理能力较低。当发生事故时，无法在第一时间获得更清晰的数据，从而无法全面了解事故情况。因此，组织救援的问题一直困扰着相关铁路工作人员。本研究旨在研究基于大数据背景下的图像识别技术对铁路交通应急管理的改进。为此，本研究提出了基于深度学习的图像识别技术，并通过重新设计铁路交通应急管理系统，以便在铁路交通问题发生时能够及时处理，同时设计了一个实验来探索图像识别的能力。实验结果表明，改进后的铁路交通应急管理系统的效率提高了 27%，识别能力提高了 64%。它可以很好地帮助当前的铁路工作人员对铁路交通安全进行应急管理。
