|本期目录/Table of Contents|

[1]何凌志,王玉珏,周月娥,等.基于改进的YOLOv5算法路面检测设计[J].工业仪表与自动化装置,2023,(04):93-97.[doi:10.19950/j.cnki.cn61-1121/th.2023.04.017]
　HE Lingzhi,WANG Yujue,ZHOU Yuee,et al.Pavement detection design based on improved YOLOv5 algorithm[J].Industrial Instrumentation & Automation,2023,(04):93-97.[doi:10.19950/j.cnki.cn61-1121/th.2023.04.017]
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基于改进的YOLOv5算法路面检测设计

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2023年04期
页码:
93-97
栏目:
出版日期:
2023-08-15

文章信息/Info

Title:
Pavement detection design based on improved YOLOv5 algorithm
文章编号:
1000-0682（2023）03-0093-05
作者:
何凌志; 王玉珏; 周月娥; 周研逸
南京理工大学 紫金学院，江苏 南京，210023
Author(s):
HE Lingzhi;  WANG Yujue;  ZHOU Yuee; ZHOU Yanyi
Nanjing University of Science and Technology Zijin college,Jiangsu Nanjing 210023,China
关键词:
深度学习; YOLOv5算法; 路面检测; 网络剪枝; 稀疏化训练; 目标检测算法; 轻量化检测网络; 嵌入式部署
Keywords:
deep learning;  YOLOv5 algorithm;  road detection;  network pruning;  sparse training;  target detection algorithm;  lightweight detection network;  embedded deployment
分类号:
TN919
DOI:
10.19950/j.cnki.cn61-1121/th.2023.04.017
文献标志码:
A
摘要:
随着大规模的公路建设，公路路面检测对于已建成公路的维护保养尤为重要，但是目前的深度学习网络模型都较大，并且部署到嵌入式端会造成Al算力不足的问题。因此，智能算法检测渐渐进入人们视野，该文设计了基于改进的YOLOv5的路面检测算法的检测设计。提出了一种通过Network Slimming网络剪枝的方法对稀疏化训练的YOLOv5目标检测算法模型进行剪枝微调，并通过tensorboard网页观察BN缩放因子直方图变化从而确定剪枝微调的比例。经过实际测试对比，相较与正常训练的算法，通过稀疏化处理后剪枝训练的YOLOv5算法模型所占权重减小了6.5 MB，对路面坑洞检测中的平均准确率（mAP）达到了81.4%，相比原始YOLOv5算法提升了2.1%，同时出现漏检现象较少，具有较好的检测精度。
Abstract:
With the large-scale highway construction, highway pavement detection is particularly important for the maintenance of completed highways. However, the current deep learning network models are large, and deployment to the embedded terminal will cause the problem of insufficient Al computing power. Therefore, intelligent algorithm detection gradually enters people’s field of vision. This paper designs a detection design based on the improved YOLOv5 road detection algorithm. This paper proposes a method of pruning the sparsely trained YOLOv5 target detection algorithm model through Network Slimming network pruning, and observes the change of the BN scaling factor histogram through the tensorboard webpage to determine the proportion of pruning and fine-tuning. After the actual test comparison, compared with the normal training algorithm, the weight of the YOLOv5 algorithm model trained by pruning after sparse processing is reduced by 6.5MB, and the average accuracy rate (mAP) in road pothole detection reaches 81.4 %, which is 2.1% higher than the original YOLOv5 algorithm. At the same time, there are fewer missed detections and better detection accuracy.

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备注/Memo

备注/Memo:
收稿日期：2022-07-01

基金项目：
2021年江苏省大学生创新创业训练计划项目（202113654016Y）；
2022年江苏省大学生创新创业训练计划项目（202213654015Y）

第一作者：
何凌志(2001—)，男，汉，本科生，主要研究方向为深度学习技术。E-mail：1607803784@qq.com

通信作者：
王玉珏（1981—)，女，学士学位(在读硕士)，讲师，主要研究领域为自动化控制及物联网技术的研究。E-mail：41040781@qq.com
更新日期/Last Update: 1900-01-01