|Table of Contents|

Traffic Video Target Detection Algorithm Based on Improved YOLOv3(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2021年02期
Page:
47-53
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Traffic Video Target Detection Algorithm Based on Improved YOLOv3
Author(s):
Liang Qinjia; Liu Huai; Lu Fei
School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023,China
Keywords:
traffic monitoring; target detection; deep learning; convolutional neural network
PACS:
TP391.4
DOI:
10.3969/j.issn.1672-1292.2021.02.008
Abstract:
Aiming at the problem that the existing target detection algorithm based on YOLOv3 is difficult to balance the speed and accuracy of multi-scale target detection,the paper proposes an improved YOLOv3 multi-scale target detection algorithm. The algorithm firstly selects the number of candidate anchor frames and the aspect ratio dimensions for each scale through k-means++ clustering,which effectively reduces the clustering deviation caused by the original algorithm at the initial clustering points. Secondly,the YOLOv3 detection scale is extended from 3 to 4,in order to improve the accuracy of target detection under different scales. Finally,in order to avoid gradient fading,the six convolutional layers before the detection layer are converted into two residual units. The experimental results on UA-DETRAC dataset show that the accuracy and recall rate of this method are 7.91% and 4.57% higher than the original YOLOv3,respectively. Meanwhile,the processing speed can meet the real-time requirements of traffic video.

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Last Update: 2021-06-30