Visual inspection of concrete structures is an important task; however, it is labor-intensive work, taking much time and effort, and it is often dangerous when inspectors work in high places. Automatic detection of deterioration such as cracks, free lime, exposed reinforcing bars, etc., from digital images has been extensively researched. Current deep learning approaches have improved the detection performance compared with conventional approaches. Most deep learning approaches use publicly available deep learning models, such as Faster R-CNN and Single Shot multiBox Detector (SSD). Although the results of deterioration detection methods have been compared with the conventional approach, the deep learning models themselves have not been compared. In this research, using the same datasets, seven deep learning models (YOLOv3, RetinaNet-50, RetinaNet-101, RetinaNet-152, SSD512, SSD300, and Faster R-CNN), were compared for detecting five types of deterioration (cracks, exposed reinforcing bars, free lime c-type, free lime d-type, and free lime e-type). YOLOv3 showed the highest mean average precision (mAP) of 85.7%, whereas the other models showed less than 80%.
Aging of social infrastructure has become a social issue in Japan. In order to use existing structures effectively, how to make maintenance and management more efficient and advanced is an important problem to tackle. For visual inspection of concrete structures such as bridges and tunnels, crack is one of the factors that cause structural deterioration and failure, and it should be evaluated accurately. Recently, many methods based on image processing and machine learning, including deep learning, have been proposed. It is expected to evaluate cracks efficiently and accurately for practical use in the field. In this article, we propose a method of applying two deep learning models. The first model, which is based on VGGNet, classifies if cracks exist or not in a local area. Another model, which is based on FCN, extracts crack pixels from the detected area. Experimental result shows that the proposed method extracts cracks automatically and accurately, compared to conventional methods.
Currently, the ground condition of the tunnel cutting face is evaluated by engineers manually. However, the evaluation of ground conditions based on the visual observation of engineers includes subjective factors. Therefore, methods that can quantitatively evaluate the ground condition of the tunnel cutting face is necessary. In this paper, a quantitative evaluation of the tunnel-cutting face’s geological condition is realized by estimating the drilling energy from the tunnel face image. We learn and update the estimator for each construction site using online learning that takes tunnel construction characteristics into account.