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IET Cyber-Systems and Robotics
IET Cyber-Systems and Robotics  2021, Vol. 3 Issue (3): 256-264    DOI: 10.1049/csy2.12029
    
An improved YOLOv3-tiny algorithm for vehicle detection in natural scenes
An improved YOLOv3-tiny algorithm for vehicle detection in natural scenes
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摘要： YOLO (You Only Look Once), as a target detection algorithm with good speed and precision, is widely used in the industry. In the process of driving, the vehicle image captured by the driving camera is detected and it extracts the license plate and the front part of the vehicle. Compared with the network structure of YOLOv3-tiny algorithm, the acquisition method of anchor box is improved by combining the Birch algorithm. In order to improve the real-time performance, the original two-scale detection is added to the multi-scale prediction of three-scale detection to ensure its accuracy. Finally, the experimental results show that the improved YOLOv3-tiny network structure proposed in this study can improve the performance of mean-average-precision, intersection over union and speed by 5.99%, 17.52% and 48.4%, respectively, and the algorithm has certain robustness.
Abstract: YOLO (You Only Look Once), as a target detection algorithm with good speed and precision, is widely used in the industry. In the process of driving, the vehicle image captured by the driving camera is detected and it extracts the license plate and the front part of the vehicle. Compared with the network structure of YOLOv3-tiny algorithm, the acquisition method of anchor box is improved by combining the Birch algorithm. In order to improve the real-time performance, the original two-scale detection is added to the multi-scale prediction of three-scale detection to ensure its accuracy. Finally, the experimental results show that the improved YOLOv3-tiny network structure proposed in this study can improve the performance of mean-average-precision, intersection over union and speed by 5.99%, 17.52% and 48.4%, respectively, and the algorithm has certain robustness.
出版日期: 2021-08-26
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作者相关文章　　
Bingqiang Huang
Haiping Lin
Zejun Hu
Xinjian Xiang
Jiana Yao

引用本文:

Bingqiang Huang, Haiping Lin, Zejun Hu, Xinjian Xiang, Jiana Yao. An improved YOLOv3-tiny algorithm for vehicle detection in natural scenes. IET Cyber-Systems and Robotics, 2021, 3(3): 256-264.

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https://www.zjujournals.com/iet-csr/CN/10.1049/csy2.12029    或    https://www.zjujournals.com/iet-csr/CN/Y2021/V3/I3/256

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