Railway Traffic Emergency Management Relying on Image Recognition Technology in the Context of Big Data

Abstract

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.

Intelligent Identification of Coal Crack in CT Images Based on Deep Learning

“…Digital images contain a lot of important information, which can be extracted in di erent ways in di erent elds. For example, it can be used in the eld of encryption technology [1,2], information security [3][4][5][6], in the eld of industrial engineering [7], in the eld of agriculture [8], and so on. Digital image processing technology includes many categories [9,10], and image segmentation is one of them.…”

Retracted: Railway Traffic Emergency Management Relying on Image Recognition Technology in the Context of Big Data

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”