Abstract
Rail transit is developing towards intelligence which takes lots of computation resource to perform deep learning tasks. Among these tasks, object detection is the most widely used, like track obstacle detection, catenary wear, and defect detection and looseness detection of train wheel bolts. But the limited computation capability of the train onboard equipment prevents running deep and complex detection networks. The limited computation capability of the train onboard equipment prevents conducting complex deep learning tasks. Cloud computing is widely utilized to make up for the insufficient onboard computation capability. However, the traditional cloud computing architecture will bring in uncertain heavy traffic load and cause high transmission delay, which makes it fail to complete real-time computing intensive tasks. As an extension of cloud computing, edge computing (EC) can reduce the pressure of cloud nodes by offloading workloads to edge nodes. In this paper, we propose an edge computing-based method. The onboard equipment on a fast-moving train is responsible for acquiring real-time images and completing a small part of the inference task. Edge computing is used to help execute the object detection algorithm on the trackside and carry most of the computing power. YOLOv3 is selected as the object detection model, since it can balance between the real-time and accurate performance on object detection compared with two-stage models. To save onboard equipment computation resources and realize the edge-train cooperative interface, we propose a model segmentation method based on the existing YOLOv3 model. We implement the cooperative inference scheme in real experiments and find that the proposed EC-based object detection method can accomplish real-time object detection tasks with little onboard computation resources.
Highlights
Over the years, the safety of railway transportation along the line is highly valued, but it is threatened by the failure of railway infrastructure, such as wrong signal light display, the physical environment changes caused by bad weather, and railway obstacles [1]
As one of the research directions of intelligent rail transit, obstacle detection based on computer vision (CV) can help to detect pedestrians, vehicles, and other obstacles on the track and ensure safe operation of train systems
In order to reduce the computation burden of on-board devices, this paper proposes an EC-based method for rail transit obstacle detection, which uploads the on-board computing tasks to the edge computing server
Summary
The safety of railway transportation along the line is highly valued, but it is threatened by the failure of railway infrastructure, such as wrong signal light display, the physical environment changes caused by bad weather, and railway obstacles [1]. In order to reduce the computation burden of on-board devices, this paper proposes an EC-based method for rail transit obstacle detection, which uploads the on-board computing tasks to the edge computing server. The data related to obstacle detection, like model structure and model parameters, can be distributed to the edge device on the train from the cloud. To achieve collaborative inference with edge computing, this paper proposes a model segmentation method based on YOLOv3, which has the backbone of darknet (53 convolutional layers). It uses k-means clustering to determine bounding box priors and uses binary cross-entropy loss for the class predictions [12].
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