| Abstract |
Visual inspection of concrete structures is an important task; however, it is labor-intensive work. Automatically detection of deterioration from digital pictures has been extensively researched. Current deep learning approaches have improved the detection 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 91.1%, whereas the other models had mAPs of approximately 80%. © 2019 American Society of Civil Engineers. |
