Research and Analysis of Railway Obstacle Detection Technology Based on Deep Learning

Abstract

The advancement of intelligent detection technologies has made highly reliable obstacle identification a critical factor in enhancing the operational safety of railways. Nevertheless, traditional detection methods (such as railside infrared counters and vehicle-mounted millimeterwave radars) are limited by inherent defects such as strong environmental sensitivity, high detection rate of small targets and no classification ability, making it difficult to meet the safety needs in complex scenarios. In recent years, deep learning has promoted the transition of railway obstacle detection technology to intelligence with its multimodal feature autonomous extraction and dynamic scene modeling capabilities. This review offers a thorough analysis of the historical development and advancements in railway obstacle detection methods, highlighting the transformative advances brought about by deep learningbased models, comprehensively compares the application characteristics and scenarios of various technologies, and discusses the application and evolution of deep learning in the two core tasks of target recognition and semantic segmentation, covering representative models such as convolutional neural network and Transformer. This article also uses the analysis of public data sets to help the new generation of railway obstacle detection technology to provide a theoretical basis and also facilitate researchers to quickly carry out experimental verification.