Collaborative Unloading Time Delay Optimization Based on Real-time Rail Detection

Rail is an important infrastructure of railway transportation system, and its safety is very important to train operation. Regular inspection of rail conditions can help detect potential defects and damages in a timely manner. In recent years, machine vision has been gradually applied to rail inspection. However, owing to the limitations of network and computing resources on railway cars, detection work can only be carried out during the nonrunning time of ordinary trains and real-time detection cannot be performed. To solve the aforementioned problems, a terminal-edge-cloud architecture is adopted. This study proposes mounting high-speed cameras on a train at certain positions. The detection image tasks collected by these cameras are carried to the terminal of the pretrained detection model (cached in advance), the edge server of the rail side, and the cloud server for processing. Based on the discrete composition of the detection tasks and considering the constraints of the detection task distribution ratio, CPU computing power, and task priority constraint time delay, the detection task time delay is used as the optimization objective to construct the objective function. Moreover, the task unloading processing problem is expressed as a maximum-minimization model problem. Finally, a Genetic Algorithm (GA) is used to obtain the optimal task allocation ratio, CPU computing power, task allocation, and minimum task time delay. The experimental results show that in the case of generating a single detection task with a train capturing frequency of 200 Hz, the response time delay based on genetic algorithm collaborative unloading is reduced by 1 287, 515, and 875 ms in terms of the binary cloud, edge, and local response time delays. In the case of 10 detection tasks, the response time delay based on genetic algorithm collaborative unloading is reduced by 2.440 and 3.520 s compared to particle swarm optimization and ant colony optimization, respectively. This method has significant time delay optimization effects in different unloading scenarios.