|Table of Contents|

Vehicle Detection Algorithm Based on Improved Mask R-CNNin Traffic Surveillance Video(PDF)

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

Issue:
2020年04期
Page:
44-50
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Vehicle Detection Algorithm Based on Improved Mask R-CNNin Traffic Surveillance Video
Author(s):
Lu Fei1; 2; Shen Shibin1; 3; Su Xiaoyun1; 2; Xie Fei1; 2; 3; Zhang Yue1; Liu Yijian1; 2; 3
(1.School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023,China)(2.Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing,Nanjing 210023,China)(3.Nanjing Industry Institute for Advanced Intelligent Equipment Co.,Ltd.,Nanjing 210023,China)
Keywords:
target detection; traffic surveillance; Mask R-CNN; mask prediction
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2020.04.007
Abstract:
Aiming at the problem of missing detection or wrong detection of target vehicles caused by occlusion in traffic surveillance video,an improved vehicle detection algorithm based on Mask R-CNN traffic surveillance video is proposed. Firstly,the backbone network based on the bottleneck structure is used to improve the ability of extracting features from the backbone network. Then,the mask branch based on the predicted mask score is used to fuse the target’s category score and mask quality score to improve the vehicle’s mask quality. Finally,the target detection loss function based on Arcface Loss can improve the discriminability between different features and improve the detection accuracy of the target. The experimental results show that the improved Mask R-CNN model can better detect the shielded vehicle,and that the detection accuracy of the target vehicle is higher than those of the Faster R-CNN,YOLO v3 and Mask R-CNN model,thus solving the problem of missing or wrong detection of the target vehicle.

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Last Update: 2020-12-15