Engineering-oriented bridge multiple-damage detection with damage integrity using modified faster region-based convolutional neural network

A bridge damage detector with preserving integrity based on modified Faster region-based convolutional neural network (R-CNN) is proposed for multiple damage types. The methodologies of dataset collection, damage annotation, and anchors generation are modified. The performance for bridge multiple-damage detectors with ResNet50 or ResNet101 as feature extraction network are compared. The results show that, with the modified Faster R-CNN, the mean average precision reaches 84.56% (76.43%) at the intersection-over-union metrics of 0.5 (0.75). We further demonstrate that the localization offset for Faster R-CNN is lower than that of YOLOv3. The modified bridge damage detector enables better detecting performance, and can preserve the damage integrity.