基于改进YOLOv3的高速公路隧道内停车检测方法

为了更准确地检测高速公路隧道内停车行为，提出一种基于改进YOLOv3车辆检测模型的高速公路隧道内停车检测方法。通过筛选VOC数据集以及实际高速公路隧道内的车辆图片制作专门用于高速公路隧道内车辆检测的数据集，选取YOLOv3目标检测模型作为车辆检测的基础网络结构，并对其进行加深网络结构的改进使其能够准确检测隧道内的车辆。将Deep SORT跟踪算法应用于改进的停车检测模型中，对车辆进行跟踪从而计算行驶速度，并创新性地设置双重速度阈值来判别车辆的停车行为。实验结果表明，经过改进的YOLOv3模型相比于原模型，在VOC-vehicle数据集和Tunnel-vehicle数据集上的mAP都有所提升，最终获得了mAP为98.19%的高速公路隧道车辆检测模型。将基于改进YOLOv3的高速公路隧道内停车检测方法在高速公路隧道视频上进行测试，可以有效地在高速公路隧道中完成停车检测的任务。

Detection Method of Highway Tunnel Vehicle Stopping Based on Improved YOLOv3

In order to detect the vehicle stopping behaviors more accurately in highway tunnel, a detection method based on the improved YOLOv3 is proposed. Firstly, a highway tunnel data set is produced and the YOLOv3 object detection model is deepened to improve the network structure and accurately detect vehicles in the tunnel. Then the Deep SORT algorithm is used to track the vehicle, and the vehicle speed is calculated. The speed threshold is used to determine whether a stopping behavior has occurred. The experimental results show that, compared with the original model, the optimized YOLOv3 model in this paper improves the mAP on VOC-vehicle data set and Tunnel-vehicle data set. Finally, a vehicle detection model with 98.19% mAP is obtained. In addition, based on the improved YOLOv3, the detection method of highway tunnel vehicle stopping is tested on the highway tunnel video and the results show that the detection of the vehicle stopping behaviors can be completed effectively.