Machine Learning for Crack Detection: Review and Model Performance Comparison
Yung‐An Hsieh, Yichang Tsai
Abstract
With the advancement of machine learning (ML) and deep learning (DL), there is a great opportunity to enhance the development of automatic crack detection algorithms. In this paper, the authors organize and provide up-to-date information on on ML-based crack detection algorithms for researchers to more efficiently seek potential focus and direction. The authors first reviewed 68 ML-based crack detection methods to identify the current trend of development, pixel-level crack segmentation. The authors then conducted a performance evaluation on 8 ML-based crack segmentation models using consistent evaluation metrics and three-dimensional (3D) pavement images with diverse conditions to identify remaining challenges and potential directions for future development. Based on the comparison results, deeper backbone networks in FCN models and skip connections in U-Net both improved the performance. Within different categories of pavement images, except for the Other Distress category, FCN and U-Net scored over 90 on the enhanced Hausdorff distance metric. Results showed that solving the false-positive problem is an important step in further improving ML-based crack detection models.