A critical review and comparative study on image segmentation-based techniques for pavement crack detection

The prompt detection of early decay in the pavement could be an auspicious technique in road maintenance. Admittedly, early crack detection allows preventive measures to be taken to avoid damage and possible failure. With regards to the advancement in computer vision and image processing in civil engineering, traditional visual inspection has been replaced by semi-automatic/automatic techniques. The process of detecting objects from the images is a fundamental stage of any image processing technique since the accuracy rate of the classification will depend heavily on the quality of the results obtained from the segmentation step. The major challenge of pavement image segmentation is the detection of thin, irregular dark lines cracks that are buried into the textured backgrounds. Although the pioneering works on image processing methodologies have proven great merit of such techniques in detecting pavement surface distresses, there is still a need for further improvement. The academic community is already working on image-based identification of pavement cracks, but there is currently no standard structure. This literature review establishes the history of development and interpretation of existing studies before conducting new research; and focuses heavily on three major types of approaches in the field of image segmentation, namely thresholding-based, edge-based, and data driven-based methods. With comparison and analysis of various image segmentation algorithms, this research provides valuable information for researchers working on enhanced segmentation strategies that potentially yield a fully automated distress detection process for pavement images with varying conditions.