林区视频监控下车流量分类统计

Traffic Classification Statistics under Video Surveillance in Forest Areas

针对林区环境中通过监控视频统计车流量的传统方法提取车辆特征困难、无法分类统计等问题,提出了一种基于YOLOv5结合DeepSORT的车流量分类统计方法。该方法使用目标检测算法YOLOv5作为检测器对车辆进行分类检测,为了提升实际场景中的车辆检测效果,在算法中融入CBAM注意力机制增强检测器对车辆的特征提取能力,同时将NMS改进为DIoU-NMS,解决了因车辆相互遮挡导致的漏检问题。使用目标跟踪算法DeepSORT对检测到的车辆进行跟踪,为了减少车辆身份切换现象,将重识别网络在车辆重识别数据集上重新训练。最后通过在视频中设置虚拟线的方法对跟踪到的车辆进行统计。将该方法在实际场景中进行效果验证,实验结果表明,总体车流量统计准确率较改进前提升10.1%,汽车、货车、客车的车流量统计准确率分别为91.8%,94.6%,93.8%。

In view of the problems such as difficulty in extracting vehicle features and inability to classify statistics by the traditional method of statistical traffic flow through surveillance video in forest environment,a method of traffic flow classification statistics based on YOLOv5 combined with DeepSORT was proposed.The method used the objective detection algorithm YOLOv5 as a detector to classify and detect vehicles.In order to improve the vehicle detection effect in the actual scene,the CBAM attention mechanism was incorporated into the algorithm to enhance the feature extraction ability of the detector for vehicles.In addition,the NMS was improved to DIoU-NMS so as to solve the problem of missed detection caused by mutual vehicle occlusion.The objective tracking algorithm DeepSORT was used to track the detected vehicles,and the reidentification network was retrained on the vehicle re-identification dataset in order to reduce the vehicle identity switching phenomenon.Finally,the tracked vehicles were counted by setting virtual lines in the video.The results of the method were verified in the actual scenario.As shown by the experimental results,the overall traffic flow statistics accuracy was improved by 10.1%compared with that before the improvement.Besides,the traffic flow statistics accuracy of cars,trucks,and buses reached 91.8%,94.6%and 93.8%respectively.