[1]汤青洲,张德津,王墨川,等. SLIC超像素与Inception网络的路面裂缝识别方法[J].湖北工业大学学报,2021,(4):8-12+51.
　TANG Qingzhou,ZHANG Dejin,WANG Mochuan,et al. Pavement Crack Detection Method Based on SLIC Superpixel and Inception Network[J].,2021,(4):8-12+51.
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 SLIC超像素与Inception网络的路面裂缝识别方法()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2021年第4期
页码:
8-12+51
栏目:
湖北工业大学学报
出版日期:
2021-08-26

文章信息/Info

Title:
 Pavement Crack Detection Method Based on SLIC Superpixel and Inception Network
文章编号:
1003-4684(2021)04-0008-05
作者:
 汤青洲1;  张德津1; 2;  王墨川1;  胡成雪1;  陶 健1
 1 湖北工业大学电气与电子工程学院，湖北 武汉 430068;
 2 深圳大学广东省城市空间信息工程重点实验室,深圳 518060
Author(s):
 TANG Qingzhou1;  ZHANG Dejin1; 2;  WANG Mochuan1;  HU Chengxue1;  TAO Jian1
 1 Shool of Electrical and Electronic Engineering, Hubei Univ. of Tech., Wuhan 430068,China;
 2 Guandong Key Laboratory for Urban Informatics, Shenzhen Univ., Shenzhen 518060,China
关键词:
 ; 计算机视觉;  深度学习;  裂缝识别;  超像素分割;  形态学
Keywords:
 ; computer vision;  deep learning;  crack detection;  superpixel segmentation;  morphology
分类号:
TP23, TP181, U8
文献标志码:
A
摘要:
 沥青路面图像存在大量点状噪声及裂缝不连续的问题，给识别精确度带来挑战，为此，提出了一种结合超像素分割与Inception网络的裂缝识别方法，首先采用超像素分割方法对路面图像进行初始分割，再利用Inception网络对超像素分割结果进行分类，实现对裂缝的粗分割及裂缝区域的准确定位；其次通过像素级与超像素级形态学方法提取裂缝主干，还原裂缝完整形态。与传统图像分割算法相比，有效降低了提取裂缝区域误差，完成裂缝快速准确定位，更适用于实际采集的路面图像裂缝识别。
Abstract:
 The method of automatic asphalt pavement crack detection is an important prerequisite for pavement maintenance. And pavement image of speckle noise and crack discontinuous problems bring recognition accuracy big challenges. To that end, this paper proposes a combination of super pixel segmentation and Inception network fracture identification methods. First of all, the super segmentation method for pavement image pixels initial segmentation, reuse Inception network classifying super pixel segmentation result, Rough segmentation of fractures and accurate positioning of fracture areas are realized. Secondly, the crack trunk was extracted by pixel-level and superpixel-level morphological methods to restore the complete morphology of the crack. Finally, compared with the traditional image segmentation algorithm, the results show that it is better than the traditional algorithm, and is more suitable for the actual road image crack detection.

参考文献/References:

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备注/Memo

备注/Memo:
［收稿日期］ 2021-04-17
［基金项目］ 国家重点研发计划(2019YFB2102703)；广东省教育厅重点领域专项(2020ZDZX1052)；深圳市科创委面上项目(纵20200125)；深圳大学青年教师科研启动项目（QNJS0138）
［第一作者］ 汤青洲(1996-)， 女，湖北荆州人，湖北工业大学硕士研究生，研究方向为图像处理
［通信作者］ 张德津(1974-)， 男，湖北桃源人，工学博士，湖北工业大学，深圳大学教授，研究方向为精密工程测量，智能交通系统
更新日期/Last Update: 2021-08-27