Pixel-level concrete bridge crack detection using Convolutional Neural Networks, gabor filters, and attention mechanisms

Abstract

Cracks are prominent defects that significantly impact concrete bridges' structural safety and serviceability assessments. The conventional manual bridge inspection is often limited by multiple challenges related to the heavy workload of data processing and the subjective judgment of inspectors. Several vision-based methods, from traditional image processing techniques to segmentation deep learning models, have been employed for pixel-level crack detection to detect cracks in various scenes and surfaces automatically. The present paper proposes an end-to-end framework for crack segmentation combining UNet, Gabor filters, and Convolutional Block Attention Modules leveraging the complementary strengths of both spatial and frequency domains to enhance crack feature extraction and mitigate the impact of image disturbances. The designed network is trained and evaluated on a multi-source annotated crack dataset established by the authors to expose the model to multiple instances of crack appearance and background complexity and enhance its generalization ability. The proposed model achieves an Intersection over Union (IoU) of 60.62 % and an F1-score of 74.49 %, outperforming benchmark segmentation networks. Experimental results demonstrate the effectiveness of cross-domain feature fusion and multi-source data utilization in segmenting cracks with diverse patterns and background interference scenarios.