Abstract
The traditional analysis method of train obstacle uses isomorphic sensors to obtain the state information and completes detection and identification analysis at the remote end of a network. A single data sample and more processing links will reduce the accuracy and speed analysis for subway encountering obstacles. To solve this problem, this paper proposes a subway obstacle perception and identification method based on cloud edge cooperation. The subway monitoring cloud platform realizes the training and construction of a detection model, and the network edge side completes the situation awareness of track state and real‐time action when the train encounters obstacles. Firstly, the railroad track position is detected by cameras, and subway running track is identified by Mask RCNN algorithm to determine the detection area of obstacles in the process of subway train running. At the edge of network, the feature‐level fusion of data collected by sensor cluster is carried out to provide reliable data support for detection work. Then, based on the DeepSort and YOLOv3 network models, the subway obstacle detection model is constructed on the subway monitoring cloud platform. Moreover, a trained model is distributed to the network edge side, so as to realize the fast and efficient perception and action of obstacles. Finally, the simulation verification is implemented based on actual collected datasets. Experimental results show that the proposed method has good detection accuracy and efficiency, which maintains 98.9% and 1.43 s for obstacle detection accuracy and recognition time in complex scenes.
Highlights
Urban rail transit is one of the most popular means of transportation for urban people, and its development speed is very rapid [1]
Based on cloud edge cooperation mode and deep learning technology, this paper proposes a fast and effective rail transit obstacle recognition method
Mask RCNN algorithm is applied to the route identification of a metro rail transit, which can provide route guarantee for the safe directional operation of trains
Summary
Urban rail transit is one of the most popular means of transportation for urban people, and its development speed is very rapid [1]. Because the subway traffic environment is mostly closed and low, the operating environment and lighting conditions are not enough to support the traditional detection methods to realize the identification of track obstacles. With the development of sensor technology, state data acquisition is based on the installation of detection devices on specific tracks [10]. A certain radar or RF device is installed at the front side of the subway train, which can collect the status data of the running track before not contacting the obstacles, upload it to the monitoring system platform for analysis and decision-making, realize effective and stable braking and deceleration, and greatly improve the operation safety. Relying on the detection and analysis of subway monitoring platform can improve the accuracy to a certain extent, but it can not meet the requirements of track foreign object identification for analysis speed
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