Subway Obstacle Perception and Identification Method Based on Cloud Edge Collaboration

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.

Edge-cloud collaborative multi-level fault diagnosis based on stacked sparse autoencoder

“…Edge-cloud collaboration has been introduced for fault diagnosis since its conceptual proposal. Several methods have been developed, including multi-level fault diagnosis [1,2] and edge-inferencing after cloud-trained [3][4][5][6][7][8][9][10][11][12][13] . Multi-level fault diagnosis is to deploy simple models at the edge for rough classification to filter normal data and reduce the inference time.…”

“…When a fault occurs, a second-level fault diagnosis is performed in the cloud to infer the details of a fault [1,2] . Edgeinferencing after cloud-trained trains a diagnostic model in the cloud and then deploys it to edge for inference [3][4][5][6][7][8][9][10] . However, a lightweight model is usually adopted due to the limited computing resources on the edge side [11][12][13] .…”

Edge-cloud collaborative transfer learning based on stacked sparse autoencoder with data regularization

“…Several edge-cloud collaborative fault diagnosis methods have been proposed, including edge-inferencing after cloudtrained [2,3], lightweight models [4,5], and hierarchical fault diagnosis [6,7]. Refs.…”

“…Refs. [2,3] proposed the method of edgeinferencing after cloud-trained, neural network models are trained in the cloud and deployed to the edge for inference. Refs.…”