Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model

In many developed countries with a long history of urbanization, there is an increasing need for automated computer vision (CV)-based inspection to replace conventional labor-intensive visual inspection. This paper proposes a technique for the automated detection of multiple concrete damage based on a state-of-the-art deep learning framework, Mask R-CNN, developed for instance segmentation. The structure of Mask R-CNN, which consists of three stages (region proposal, classification, and segmentation) is optimized for multiple concrete damage detection. The optimized Mask R-CNN is trained with 765 concrete images including cracks, efflorescence, rebar exposure, and spalling. The performance of the trained Mask R-CNN is evaluated with 25 actual test images containing damage as well as environmental objects. Two types of metrics are proposed to measure localization and segmentation performance. On average, 90.41% precision and 90.81% recall are achieved for localization and 87.24% precision and 87.58% recall for segmentation, which indicates the excellent field applicability of the trained Mask R-CNN. This paper also qualitatively discusses the test results by explaining that the architecture of Mask R-CNN that is optimized for general object detection purposes, can be modified to detect long and slender shapes of cracks, rebar exposure, and efflorescence in further research.