GMDNet: An Irregular Pavement Crack Segmentation Method Based on Multi-Scale Convolutional Attention Aggregation

Author:

Qi Yawei1ORCID,Wan Fang1,Lei Guangbo1,Liu Wei1,Xu Li1,Ye Zhiwei1,Zhou Wen1

Affiliation:

1. School of Computer Science, Hubei University of Technology, Wuhan 430068, China

Abstract

Pavement cracks are the primary type of distress that cause road damage, and deep-learning-based pavement crack segmentation is a critical technology for current pavement maintenance and management. To address the issues of segmentation discontinuity and poor performance in the segmentation of irregular cracks faced by current semantic segmentation models, this paper proposes an irregular pavement crack segmentation method based on multi-scale convolutional attention aggregation. In this approach, GhostNet is first introduced as the model backbone network for reducing parameter count, with dynamic convolution enhancing GhostNet’s feature extraction capability. Next, a multi-scale convolutional attention aggregation module is proposed to cause the model to focus more on crack features and thus improve the segmentation effect on irregular cracks. Finally, a progressive up-sampling structure is used to enrich the feature information by gradually fusing feature maps of different depths to enhance the continuity of segmentation results. The experimental results on the HGCrack dataset show that GMDNet has a lighter model structure and higher segmentation accuracy than the mainstream semantic segmentation algorithms, achieving 75.16% of MIoU and 84.43% of F1 score, with only 7.67 M parameters. Therefore, the GMDNet proposed in this paper can accurately and efficiently segment irregular cracks on pavements that are more suitable for pavement crack segmentation scenarios in practical applications.

Funder

National Natural Science Foundation of China

Science and Technology Research Project of Education Department of Hubei Province

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3