广东工业大学学报 ›› 2022, Vol. 39 ›› Issue (06): 73-79.doi: 10.12052/gdutxb.210038

• 综合研究 • 上一篇    下一篇

高分辨率桥梁裂缝图像实时检测

刘信宏, 苏成悦, 陈静, 徐胜, 罗文骏, 李艺洪, 刘拔   

  1. 广东工业大学 物理与光电工程学院, 广东 广州 510006
  • 收稿日期:2021-03-10 出版日期:2022-11-10 发布日期:2022-11-25
  • 通信作者: 苏成悦(1961-)，男，教授，博士，主要研究方向为应用物理、图像识别，E-mail：cysu@gdut.edu.cn
  • 作者简介:刘信宏(1995-)，男，硕士研究生，主要研究方向为图像处理、模式识别，E-mail：liuxinhong@mail2.gdut.edu.cn
  • 基金资助:
    广东省科技计划项目(2017A020208063)；广州市科技计划项目(201804010384)

Real Time Detection of High Resolution Bridge Crack Image

Liu Xin-hong, Su Cheng-yue, Chen Jing, Xu Sheng, Luo Wen-jun, Li Yi-hong, Liu Ba   

  1. School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-03-10 Online:2022-11-10 Published:2022-11-25

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摘要： 针对现有桥梁裂缝检测算法实时性弱、可靠性差等问题，提出一种嵌入式平台上的实时检测算法。使用移动平均法粗分割，依据几何特征和区域生长法再分割，定位候选裂缝片段；基于裂缝先验条件，建立双判别准则的裂缝聚合模型，递归合并裂缝片段并抑制干扰。实验表明，算法能有效提取细小裂缝，抑制不均匀光照和污渍等复杂背景干扰，识别性能与数种现有算法相比提高115%以上；在嵌入式开发板上处理1500万像素的图像仅耗时1.73 s。

关键词: 图像处理, 桥梁裂缝, 高分辨率, 实时检测, 嵌入式平台

Abstract: The problem of current algorithms is lack of timeliness and reliability in bridge crack detection. In this paper, a real-time algorithm on embedded platform is proposed. Firstly, moving average method is used to segment the image coarsely. Then, candidate crack fragments are selected by region growing method using the geometric features of their contours. Finally, a crack merging model with two criteria is built to merge the crack fragments recursively and suppress interference, based on the prior condition of bridge cracks. Experimental results show that the proposed method performs better than several existing methods by 115% at least, especially on extracting hairline cracks and complex cases with uneven illumination and dirty mark. Dealing with an image with 15 megapixel on embedded platform, it costs only 1.73 s.

Key words: image processing, bridge crack, high resolution, real-time detection, embedded platform

中图分类号: 

  • TP391
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