Road vehicle detection based on improved YOLOv3-SPP algorithm

被引：0

作者：

Wang T. ^{[1
,2
]}

Feng H. ^{[1
,2
]}

Mi R. ^{[3
]}

Li L. ^{[3
]}

He Z. ^{[4
]}

Fu Y. ^{[1
,2
]}

Wu S. ^{[1
,2
]}

机构：

[1] School of Information and Communication Engineering, Beijing Information Science & Technology University, Beijing

[2] Key Laboratory of Optoelectronic Testing Technology and Instrument, Ministry of Education, Beijing Information Science & Technology University, Beijing

[3] National Computer Network Emergency Response Technical Team, Coordination Center of China, Beijing

[4] Hebei Key Laboratory of Agricultural Big Data, Hebei Agricultural University, Baoding

来源：

Tongxin Xuebao/Journal on Communications | 2024年 / 45卷 / 02期

基金：

中国国家自然科学基金;

关键词：

activation function; atrous convolution; deep learning; vehicle detection; YOLOv3-SPP algorithm;

D O I：

10.11959/j.issn.1000-436x.2024046

中图分类号：

学科分类号：

摘要：

Aiming at the problem of low detection accuracy or missing detection caused by dense vehicles and small scale of distant vehicles in the visual detection of urban road scenes, an improved YOLOv3-SPP algorithm was proposed to optimize the activation function and take DIOU-NMS Loss as the boundary frame loss function to enhance the expression ability of the network. In order to improve the feature extraction ability of the proposed algorithm for small targets and occluding targets, the void convolution module was introduced to increase the receptive field of the target. Based on the experimental results, the proposed algorithm improves the mAP by 1.79% when detecting vehicle targets, and also effectively reduce the missing phenomenon when detecting tight vehicle targets. © 2024 Editorial Board of Journal on Communications. All rights reserved.

引用

页码：68 / 78

页数：10

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