Due to their wide applicability in inspection of concrete structures, there is considerable interest in the development of automated crack detection method by image processing. However, the accuracy of existing methods tends to be influenced by the existence of traces of tie-rod holes and formworks. In order to reduce these influences, this paper proposes a crack detection method based on semantic segmentation by deep learning. The accuracy of developed method is investigated by the photos of concrete structures with lots of adverse conditions including shadow and dirt, and it is found that not only the crack region could be detected but also the trace of tie-rod holes and formworks could be removed from the detection result with high accuracy. This paper is the English translation from the authors' previous work [Yamane, T. and Chun, P., (2019). “Crack detection from an image of concrete surface based on semantic segmentation by deep learning.” Journal of Structural Engineering, 65A, 130-138. (in Japanese)].

由于它们在混凝土结构检查中的广泛适用性，因此对通过图像处理的自动裂缝检测方法的开发非常感兴趣。但是，现有方法的准确性往往会受到拉杆孔和模板痕迹的影响。为了减少这些影响，本文提出了一种基于深度学习语义分割的裂缝检测方法。通过对有很多阴影和污垢等不利条件的混凝土结构照片的研究，对开发方法的准确性进行了研究，发现不仅可以检测出裂纹区域，而且可以消除拉杆孔和模板的痕迹检测结果的准确性高。本文是作者先前工作的英文翻译[Yamane，T. and Chun，P.，（2019）。“基于通过深度学习进行语义分割的混凝土表面图像裂缝检测。”结构工程学报，65A，130-138。（日语）]。