Abstract：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.
Tian W G, Xv H L, Yin R F, et al. Design of Intelligent Traffic Flow Detection System Based on Geomagnetic Sensor［J］. Chinese Journal of Sensors and Actuators, 2021,34(1):137-142.
Ding S N,Zhang Q J. Image registration method based on improved SIFT algorithm ［J］. Transducer and Microsystem Technologies, 2020,39(10):45-47.
［4］
Lekha S , Suchetha M . A Novel 1-D Convolution Neural Network With SVM Architecture for Real-Time Detection Applications［J］. IEEE Sensors Journal, 2017, 18(99):724-731.
［7］
Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector［C］//European conference on computer vision. Springer, Cham, 2016:21-37.
Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module［C］//Proceedings of the European conference on computer vision (ECCV). 2018: 3-19.
Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014:580-587.
［10］
Wojke N, Bewley A, Paulus D. Simple online and realtime tracking with a deep association metric［C］//2017 IEEE international conference on image processing (ICIP). IEEE, 2017: 3645-3649.
Zhang Y, Lu H Z, Zhang L P, et al. Overview of Visual Multi-object Track Algorithms with Deep Learning［J］. Computer Engineering and Applications, 2021,57(13):55-66.
Chen J Q, Jin X H, Wang W Y, et al. Vehicle Flow Detection Based on YOLOv3 and DeepSort［J］. Acta Metrologica Sinica, 2021, 42(6):718-723.
［3］
Kim J, Baek J, Kim E. A Novel On-Road Vehicle Detection Method Using πHOG［J］. IEEE transactions on intelligent transportation systems, 2015, 16(6):3414-3429.
［8］
Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection［C］//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.
［12］
Kim S J, Nam J Y, Ko B C. Online tracker optimization for multi-pedestrian tracking using a moving vehicle camera［J］. IEEE Access, 2018, 6:48675-48687.
Zheng Z, Wang P, Liu W, et al. Distance-IoU loss: Faster and better learning for bounding box regression［C］//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(07): 12993-13000.
Cheng S H, Xie W R, Zhang D F, et al. Fall Action Recognition Based on Computer Vision［J］. Acta Metrologica Sinica, 2022, 43(1):107-113.
Bewley A, Ge Z, Ott L, et al. Simple online and realtime tracking［C］//2016 IEEE international conference on image processing (ICIP). IEEE, 2016: 3464-3468.
［17］
Wojke N, Bewley A. Deep cosine metric learning for person reidentification［C］//2018 IEEE winter conference on applications of computer vision (WACV). IEEE, Lake Tahoe, NV, USA, 2018:748-756.
［6］
He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition［J］. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9):1904-1916.
［14］
Guo M H, Xu T X, Liu J J, et al. Attention mechanisms in computer vision: A survey［J］. Computational Visual Media, 2022:1-38.
2023, Vol. 44 
