An Improved YOLOv3-Tiny Traffic Detection Algorithm

doi:10.3969/j.issn.1006-2475.2020.03.021

Abstract

Abstract: The simplified version YOLOv3-Tiny of YOLO series algorithm has a relatively simple network framework and low requirement for GPU display and memory. Although the algorithm has high real-time performance but accuracy is low, it can not get accurate results in identifying driving targets. This paper first changes the size of the input pictures in order to obtain more lateral information of the pictures so that the network can easily learn the driving information. Secondly, the network structure of the algorithm is improved so as to improve the accuracy of the algorithm. Finally, the improved YOLOv3-Tiny algorithm is obtained. The experimental results show that the improved algorithm improves the accuracy while guaranteeing real-time performance.

Key words: deep learning, vehicle detection, YOLOv3-Tiny, clustering

CLC Number:

TP391

LIU Li-ran 1, CAO Jie2, YANG Lei1, QIU Nan-hao1. An Improved YOLOv3-Tiny Traffic Detection Algorithm[J]. Computer and Modernization, 2020, 0(03): 108-.

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