激光与光电子学进展, 2020, 57 (22): 221023, 网络出版: 2020-11-09   

融合深度学习聚类分割和形态学的混凝土表面裂缝量化识别 下载: 1165次

Quantitative Identification of Concrete Surface Cracks Based on Deep Learning Clustering Segmentation and Morphology
作者单位
1 武汉理工大学安全与应急管理学院, 湖北 武汉 430079
2 重庆市计量质量检测研究院, 重庆 404100
摘要
混凝土结构在施工与使用的过程中易产生各种形式的裂缝,由此会产生诸多安全问题。传统的人工安全检测方法,不仅耗费财力和时间,而且无法保障其检测精度。为了提高混凝土表面裂缝的识别效率,提出了一种基于卷积神经网络结合聚类分割的识别方法,实现了对较复杂背景下混凝土表面裂缝图像的准确识别。研究结果显示,该方法不仅能够高效地分类,还能够高精度地对较复杂背景下的裂缝进行识别,这为降低混凝土表面裂缝识别的工作量、维护混凝土结构,对其进行安全检测提供了理论依据,同时也为以后更高精度和更复杂条件下的裂缝识别研究提供了一些参考。
Abstract
Various forms of cracks can easily occur during the construction and use of concrete structures, leading to many security problems. The traditional manual safety detection method not only consumes financial resources and time but also provides no guarantee of accuracy. To improve the efficiency of crack recognition on a concrete surface, a recognition method based on convolutional neural network combined with clustering segmentation is proposed herein, which achieves accurate recognition of concrete surface crack images under more complex backgrounds. Results show that the proposed method can not only efficiently classify but also identify cracks in more complex backgrounds with high accuracy. In addition, the proposed method provides a certain theoretical basis for the workload reduce of crack recognition on concrete surfaces, as well as the maintenance and safety inspection of concrete structures. Furthermore, the proposed method provides references for future fracture-identification studies under higher accuracy and more complex conditions.

杨杰文, 章光, 陈西江, 班亚. 融合深度学习聚类分割和形态学的混凝土表面裂缝量化识别[J]. 激光与光电子学进展, 2020, 57(22): 221023. Jiewen Yang, Guang Zhang, Xijiang Chen, Ya Ban. Quantitative Identification of Concrete Surface Cracks Based on Deep Learning Clustering Segmentation and Morphology[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221023.

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