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.
Abstract-High-speed railway is seeking for a higher safety level than other public transports because of more passengers and higher speed. However, sleepiness and fatigue occur more frequently for train drivers due to longer operation time and irregular shift schedule. In the paper, we focus on study the particularity of train drivers' fatigue under the environment of high-speed railway. We observed operation performance and evaluated the degree of fatigue using train drivers' visual characteristics, while they conducted various tasks on high-speed railway driver simulator. By analyzing self-report sleepiness scales, detection distance and eye movement data, we found that pupil diameter, blink time and fixation percentage of fatigue drivers were significantly different from that of sober drivers. Meanwhile, we revealed obvious individual difference of train drivers and proposed a personalized database for train drivers' fatigue identification. These quantitative results can be used as a preliminary study for designing human-train interface of highspeed railway.
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