Road surface crack detection based on CNN

ZHAO Shan-shan; HE Ning

doi:10.13873/j.1000-9787(2017)11-0135-04

Chinese

您当前的位置：

首页 >

文章列表页 >

Road surface crack detection based on CNN

更新时间：2025-09-11

- Road surface crack detection based on CNN
- Vol. 36, Issue 11, Pages: 135-138(2017)
- 作者机构：
  
  1. 北京联合大学北京市信息服务工程重点实验室
  2. 北京联合大学智慧城市学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2017)11-0135-04
  CLC： TP183;TP391.41
- Published：2017
- 稿件说明：
移动端阅览
[1]赵珊珊,何宁.基于卷积神经网络的路面裂缝检测[J].传感器与微系统,2017,36(11):135-138.

ZHAO Shan-shan, HE Ning. Road surface crack detection based on CNN[J]. 2017, 36(11): 135-138.
[1]赵珊珊,何宁.基于卷积神经网络的路面裂缝检测[J].传感器与微系统,2017,36(11):135-138. DOI： 10.13873/j.1000-9787(2017)11-0135-04.

ZHAO Shan-shan, HE Ning. Road surface crack detection based on CNN[J]. 2017, 36(11): 135-138. DOI： 10.13873/j.1000-9787(2017)11-0135-04.

摘要

对路面图像块预标记

根据预标记结果对路面图像进行强度归一化预处理

在保留裂缝信息的同时

减少背景光照不均的影响。将预处理后的路面图像输入卷积神经网络（CNN）模型实现路面图像裂缝的检测。由于路面裂缝分布复杂

在训练网络时

使用不同尺度和不同角度的路面图像进行模型训练

使得网络能够检测不同裂缝形状。实验结果显示:裂缝检测结果较好。

Abstract

Road surface crack detection method based on convolutional neural network（ CNN） is proposed.Intensity normalization preprocessing on road surface image according to pre-labeled result

which can reduce the influence caused by the un-illumination background and the white pixel. The CNN is used to realize the crack detection. Use different scales and angles of road image for model training to improve the ability to detect small and scattered road crack. The experimental result shows that this method is effective.

关键词

Keywords

references

基于图像处理技术的混凝土路面裂缝检测方法 [J]. 郭全民,张海先. 传感器与微系统 . 2013(04)

基于多级去噪模型的路面裂缝检测方法 [J]. 王兴建,秦国锋,赵慧丽. 计算机应用 . 2010(06)

路面图像增强的多偏微分方程融合法 [J]. 唐磊,赵春霞,王鸿南,邵文泽. 中国图象图形学报 . 2008(09)

Views

489

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on restoration technology of motion blur image

Research progress of MOFs-based aptamer sensors in detection of tumor markers

Preparation of H₂V₁₂O₃₁/BiOI/FTO photoelectrochemicalsensor and its detection of tetracycline hydrochloride

Lightweight bubble detection algorithm for quartz crucibles based on improved YOLOv5

Research on improved median filtering algorithm fused with gradient information

Related Author

CHEN Ying

HONG Chenfeng

QUE Longbin

Gu Xin

PAN Hongzhi

LI Li

WU Jiahui

ZHAI Liying

Related Institution

School of Software,Nanchang Hangkong University

School of Health Science and Engineering,University of Shanghai for Science and Technology

2.Collaborative Research Center,Shanghai University of Medicine and Health Sciences

School of Chemistry and Chemical Engineering,Northeast Petroleum University

School of Communication and Information Engineering,Xi’an University of Science and Technology

⁰