[1]徐 丽,刘星星,屈立成.基于 YOLO v4 的夜间车辆检测模型轻量化研究[J].计算机技术与发展,2022,32(03):84-89.[doi:10. 3969 / j. issn. 1673-629X. 2022. 03. 014]
　XU Li,LIU Xing-xing,QU Li-cheng.Research on Lightweight of Night Vehicle Detection Model Based on YOLO v4[J].,2022,32(03):84-89.[doi:10. 3969 / j. issn. 1673-629X. 2022. 03. 014]
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基于 YOLO v4 的夜间车辆检测模型轻量化研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年03期
页码:
84-89
栏目:
图形与图像
出版日期:
2022-03-10

文章信息/Info

Title:
Research on Lightweight of Night Vehicle Detection Model Based on YOLO v4
文章编号:
1673-629X(2022)03-0084-06
作者:
徐 丽; 刘星星; 屈立成
长安大学 信息工程学院,陕西 西安 710000
Author(s):
XU Li; LIU Xing-xing; QU Li-cheng
School of Information Engineering,Chang’an University,Xi’an 710000,China
关键词:
夜间车辆检测; YOLO v4; MobileNet; 深度可分离卷积; 通道剪枝
Keywords:
night vehicle detection; YOLO v4; MobileNet; depth separable convolution; channel pruning
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 03. 014
摘要:
基于 YOLO v4 的夜间车辆检测模型轻量化研究
Abstract:
In response to the real-time requirements of the night vehicle detection model,based on the YOLO v4 model,the backbone feature extraction network is changed to MobileNet V2,which is flexible and easy to implement,and changes? ?all the ordinary convolutions in the enhanced feature extraction network to deep separable convolutions. At the same time,the model introduces a scaling factor to each channel and multiplies it with the channel input. Then the scaling? factor regular term and the weight loss function are combined for sparse regularization training. At this time,a smaller scaling factor is selected for channel pruning. After pruning,some channels of the model are missing,and the detection performance will be reduced,so the model is fine-tuned to compensate for the loss of accuracy,and after the performance evaluation,the pruning iterations are performed. Finally,a lightweight vehicle detection model is obtained,which makes the detection speed faster and can better meet the real-time requirements of night vehicle detection. The experimental analysis on the UA-DETRAC data set shows that the detection accuracy of the lightweight night vehicle detection model can reach 98. 29% ,and the number of frames per second can be as high as 42 images.

相似文献/References:

[1]郭言信,朱明旱,张明月,等.基于视频的夜间车辆检测与跟踪[J].计算机技术与发展,2020,30(05):206.[doi:10. 3969 / j. issn. 1673-629X. 2020. 05. 039]
　GUO Yan-xin,ZHU Ming-han,ZHANG Ming-yue,et al.Video-based Nighttime Vehicle Detection and Tracking[J].,2020,30(03):206.[doi:10. 3969 / j. issn. 1673-629X. 2020. 05. 039]

更新日期/Last Update: 2022-03-10