Advanced crack detection and segmentation on bridge decks using deep learning

Detecting and measuring cracks on a bridge deck is crucial for preventing further damage and ensuring safety. However, manual methods are slow and subjective, highlighting the need for an efficient solution to detect and measure crack length and width. This study proposes a novel process-based deep learning approach for detecting and segmenting cracks on the bridge deck. Five state-of-the-art object detection networks were evaluated for their performance in detecting cracks: Faster RCNN-ResNet50, Faster RCNN-ResNet101, RetinaNet-ResNet50, RetinaNet-ResNet101, and YOLOv7. Additionally, two object segmentation networks, U-Net, and pix2pix, were optimized by experimenting with various network depths, activation functions, loss functions, and data augmentation to segment the detected cracks. The results showed that YOLOv7 outperformed both Faster RCNN and RetinaNet with both ResNet50 and ResNet101 backbones in terms of both speed and accuracy. Furthermore, the proposed U-Net is better than the mainstream U-Net and pix2pix networks. Based on these results, YOLOv7 and the proposed U-Net are integrated for detecting and segmenting cracks on a bridge deck. The proposed method was then applied to two bridges in South Korea to test its performance, and the results showed that it could detect crack length with an accuracy of 92.38 percent. Moreover, the proposed method can determine crack width and classify it with an R2 value of 0.87 and an average accuracy of 91 percent, respectively. In summary, this study provides an efficient and reliable method for detecting, measuring, and classifying cracks on a bridge deck surface.