Automated bridge surface crack detection and segmentation using computer vision-based deep learning model

Bridge maintenance will become a widespread trend in the engineering industry as the number of bridges grows and time passes. Cracking is a common problem in bridges with concrete structures. Allowing it to expand will result in significant economic losses and accident risks This paper proposed an automatic detection and segmentation method of bridge surface cracks based on computer vision deep learning models. First, a bridge surface crack detection and segmentation dataset was established. Then, according to the characteristics of the bridge, we improved the You Only Look Once (YOLO) algorithm for bridge surface crack detection. The improved algorithm was defined as CR-YOLO, which can identify cracks and their approximate locations from multi-object images. Subsequently, the PSPNet algorithm was improved to segment the bridge cracks from the non-crack regions to avoid the visual interference of the detection algorithm. Finally, we deployed the proposed bridge crack detection and segmentation algorithm in an edge device. The experimental results show that our method outperforms other baseline methods in generic evaluation metrics and has advantages in Model Size(MS) and Frame Per Second (FPS).

Automated summary

This research proposes an automatic detection and segmentation method for bridge surface cracks based on computer vision deep learning models, which is able to effectively identify and segment bridge cracks. Experimental results demonstrate that our method outperforms other baseline methods, with smaller model size and higher frame per second (FPS) performance.