An improved YOLOv3‐tiny algorithm for vehicle detection in natural scenes. Issue 3 (26th August 2021)

• Record Type: Journal Article
• Authors: Huang, Bingqiang; Lin, Haiping; Hu, Zejun; Xiang, Xinjian; Yao, Jiana
• Is Part Of: IET cyber-systems and robotics. Volume 3:Issue 3(2021)
• Page Start: 256
• Page End: 264
• Publication Date: 2021-08-26
• Subjects: Robotics -- Periodicals; Cybernetics -- Periodicals; Cybernetics; Robotics; Periodicals; 629
• DOI: 10.1049/csy2.12029
• Languages: English
• ISSNs: 2631-6315

Abstract

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.