Relative-Breakpoint-Based Crack Annotation Method for Lightweight Crack Identification Using Deep Learning Methods

Abstract

After years of service, bridges could lose their expected functions. Considering the significant number of bridges and the adverse inspecting environment, the urgent requirement for timely and efficient inspection solutions, such as computer vision techniques, have been attractive in recent years, especially for those bridge components with poor accessibility. In this paper, a lightweight procedure for bridge apparent-defect detection is proposed, including a crack annotation method and crack detection. First of all, in order to save computational costs and improve generalization performance, we propose herein a relative-breakpoint annotation method to build a crack instance segmentation dataset, a critical process for a supervised vision-based crack detection method. Then, the trained models based on classic Mask RCNN and Yolact are transferred to evaluate the effectiveness of the proposed method. To verify the correctness, universality and generality of the proposed crack-detection framework, approximately 800 images are used for model training, while nearly 100 images are saved for validation. Results show that the crack instance segmentation model can achieve a level of 90% for both accuracy and recall values, with a limited dataset.