计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 192-196.doi: 10.11896/jsjkx.210100164

• 计算机图形学&多媒体 • 上一篇    下一篇

基于空洞卷积和多特征融合的混凝土路面裂缝检测

瞿中, 陈雯   

  1. 重庆邮电大学计算机科学与技术学院 重庆400065
  • 收稿日期:2021-01-24 修回日期:2021-05-08 出版日期:2022-03-15 发布日期:2022-03-15
  • 通讯作者: 瞿中(quzhong@cqupt.edu.cn)
  • 基金资助:
    国家自然科学基金(61701060)

Concrete Pavement Crack Detection Based on Dilated Convolution and Multi-features Fusion

QU Zhong, CHEN Wen   

  1. College of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Received:2021-01-24 Revised:2021-05-08 Online:2022-03-15 Published:2022-03-15
  • About author:QU Zhong,born in 1972,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include digital image processing and cloud computing.
  • Supported by:
    National Natural Science Foundation of China(61701060).

摘要: 混凝土路面的裂缝检测是确保道路安全的重要基础任务。针对混凝土路面的复杂背景和裂缝本身复杂的拓扑结构,提出了一种基于空洞卷积和多特征融合的混凝土路面裂缝检测网络,该网络采用基于U-Net的编码-解码结构。在编码阶段,使用改进的残差网络Res2Net提高特征提取能力;在网络的中间部分,使用串联和并联相结合的不同空洞率的空洞卷积,从而在增加特征点的感受野的同时不会降低特征图的分辨率;在解码阶段,融合了从低层卷积到高层卷积的多尺度和多级特征,提高了裂缝检测的准确性。为证明所提算法的有效性和准确性,将其与现有的部分检测方法进行了比较并使用F-score来评估检测性能。在多个混凝土路面数据集上的实验结果表明,该算法提高了裂缝检测的准确性,具有较好的鲁棒性。

关键词: 编码-解码结构, 残差网络, 多特征融合, 空洞卷积, 裂缝检测

Abstract: Crack detection for concrete pavement is an important fundamental task to ensure the safety of the road.Due to the complicated concrete pavement background and the diversity of cracks,a novel crack detection network of concrete pavement based on dilated convolution and multi-features fusion is proposed.The proposed network is based on the encoding-decoding structure of U-Net.In the encoding stage,the improved residual network Res2Net can be used to improve the ability of feature extraction.A cascade and parallel mode dilates convolution as center part,it can enlarge the receptive field of feature points,but without reducing the resolution of the feature maps.The decoder aggregates multi-scale and multi-level features from the low convolutional layers to the high-level convolutional layers,which improves the accuracy of crack detection.We use F-score to eva-luate our network performance.To demonstrate the validity and accuracy of the proposed method,we compare it with existing methods.The experiment results in multiple crack datasets reveal that our method is superior to these methods.The algorithm improves the accuracy of crack detection and has good robustness.

Key words: Crack detection, Dilated convolution, Encoding-decoding structure, Multi-features fusion, residual network

中图分类号: 

  • TP391.41
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