The invention discloses an intersection vehicle detection method in a fog computing environment, and the method comprises the steps: improving a YOLOv5n network through employing an R-D module and a re-parameterization SPP, training the improved YOLOv5n network through employing a vehicle detection data set, and obtaining a vehicle detection model; training a DeepSORT feature extraction network by using the training vehicle tracking data set to obtain a multi-target tracking model; a fog platform architecture is built, vehicles passing through all road intersections serve as a data generation layer, cameras, vehicle detectors, vehicle detection models and multi-target tracking models are deployed at all the road intersections to serve as fog layers, and the vehicle detectors call the vehicle detection models and the multi-target tracking models at all the road intersections. Vehicle identification detection and tracking are realized, and the detection efficiency and precision are improved. The invention further provides a bus priority passing method, and efficient control over bus priority passing at the intersection is achieved.
一种雾计算环境下的交叉路口车辆检测方法,使用R‑D模块和重参数化SPP改进YOLOv5n网络,利用车辆检测数据集训练改进的YOLOv5n网络,得到车辆检测模型;利用训练车辆跟踪数据集训练DeepSORT特征提取网络,得到多目标跟踪模型;搭建雾平台架构,其中,通过各个道路交叉路口的车辆为数据生成层,在各个道路交叉路口分别部署摄像头、车辆检测器、车辆检测模型和多目标跟踪模型作为雾层,在每个道路交叉路口,车辆检测器调用车辆检测模型和多目标跟踪模型,实现车辆识别检测与跟踪,提高检测效率和精度。本发明还提供了一种公交车优先通行方法,实现交叉路口公交优先通行的高效控制。
Intersection vehicle detection and bus priority passing method in fog computing environment
雾计算环境下的交叉路口车辆检测和公交优先通行方法
2023-09-15
Patent
Electronic Resource
Chinese
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