Road vehicle detection method based on improved YOLO v3 model and deep-SORT algorithm

MA Yong-jie; MA Yun-ting; CHENG Shi-sheng; MA Yi-de

doi:10.19818/j.cnki.1671-1637.2021.02.019

Volume 21 Issue 2

Aug. 2021

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Article Navigation > Journal of Traffic and Transportation Engineering > 2021 > 21(2): 222-231

MA Yong-jie, MA Yun-ting, CHENG Shi-sheng, MA Yi-de. Road vehicle detection method based on improved YOLO v3 model and deep-SORT algorithm[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 222-231. doi: 10.19818/j.cnki.1671-1637.2021.02.019

Citation:

MA Yong-jie, MA Yun-ting, CHENG Shi-sheng, MA Yi-de. Road vehicle detection method based on improved YOLO v3 model and deep-SORT algorithm[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 222-231. doi: 10.19818/j.cnki.1671-1637.2021.02.019

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Road vehicle detection method based on improved YOLO v3 model and deep-SORT algorithm

doi: 10.19818/j.cnki.1671-1637.2021.02.019

1.
College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, Gansu, China
2.
School of Information Science and Engineering, Lanzhou University, Lanzhou 730030, Gansu, China

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National Natural Science Foundation of China 62066041

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Author Bio:
MA Yong-jie(1967-), male, professor, PhD, myjmyj@nwnu.edu.cn
Received Date: 2020-11-23
Publish Date: 2021-04-01

Abstract

Abstract

A vehicle detection method based on the improved YOLO v3 model and deep-SORT algorithm was proposed to address the problems of serious occlusion and high misdetection rate of small target vehicles in the real-time detection of road vehicles. To improve the detection ability of the model for road vehicle, the K-means++ clustering algorithm was used to cluster the target candidate boxes, the appropriate number of anchor boxes was selected, and a feature extraction layer to the shallow layer of the network was added to extract more refined vehicle features. The robustness of the network for different distant targets was enhanced by retaining the original YOLO v3 model's output layer but adding another layer to it. After the 52 pixel×52 pixel output feature map was upsampled, a 104 pixel×104 pixel feature map was obtained, which was spliced with a shallow layer feature map of the same size to achieve the vehicle target detection. To reduce the influence of target occlusion on the detection and improve the attention to the association information between the upper and lower frames of the video, the YOLO v3 model was improved and combined with the deep-SORT algorithm to compensate for their shortcomings. Experimental results show that the improved YOLO v3 model can enhance the vehicle detection performance. Compared with the model adding feature extraction layer in the shallow layer of the network, the average accuracy improves by 1.4%, and compared with the model adding one output layer, the average accuracy improves by 0.8%. It indicates that the improved YOLO v3 model has a stronger feature expression ability and enhances the network's ability to detect small targets. After the deep-SORT algorithm is introduced into the improved YOLO v3 model, the precision and recall are 90.16% and 91.34%, respectively. Compared with the improved YOLO v3 model, the precision and recall increase by 1.48% and 4.20%, respectively. At the same time, the detection speed is maintained, and the detection of different-sized targets is highly robust. 4 tabs, 5 figs, 32 refs.
- traffic image recognition,
- convolutional neural network,
- vehicle detection,
- YOLO v3 model,
- deep-SORT algorithm,
- K-means++ clustering algorithm

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References(32)

References

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Road vehicle detection method based on improved YOLO v3 model and deep-SORT algorithm

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Road vehicle detection method based on improved YOLO v3 model and deep-SORT algorithm

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