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.

Image segmentation is the crucial step in automatic image distress detection and classification (e.g., types and severities) and has important applications for automatic crack sealing. Although many researchers have developed pavement distress detection and recognition algorithms, full automation has remained a challenge. This is the first paper that uses a scoring measure to quantitatively and objectively evaluate the performance of six different segmentation algorithms. Up-to-date research on pavement distress detection and segmentation is comprehensively reviewed to identify the research need. Six segmentation methods are then tested using a diverse set of actual pavement images taken on interstate highway I-75/I-85 near Atlanta and provided by the Georgia Department of Transportation with varying lighting conditions, shadows, and crack positions to differentiate their performance. The dynamic optimization-based method, which was previously used for segmenting low signal-to-noise ratio (SNR) digital ra...

After decades of research and development, a fully automated system for pavement crack detection with an intensity-based two-dimensional (2D) imaging data acquisition system under different lighting and low intensity contrast conditions still remains a challenge. With the advances of sensor technology, a three-dimensional (3D) laser technology that can collect high-resolution 3D continuous transverse pavement profiles for detecting cracks on the basis of their 3D elevation rather than 2D intensity has become available. This study, sponsored by the U.S. Department of Transportation (US DOT) Research Innovative Technology Administration (RITA) program, evaluates the feasibility of using emerging 3D laser technology to detect cracks under different lighting and poor intensity contrast conditions. For this purpose, 3D surface data from laboratory tests on cracks ranging from 1 to 5mm wide and tests on the actual pavement cracks of different widths and depths and under different lighting conditions wer...

Potholes are one of the roadway distresses that negatively impact roadway safety. With emerging sensing technology, three-dimensional (3D) pavement data, derived using 3D laser technology, have become available for detecting cracking and rutting. This paper presents a pothole detection method using 3D pavement data and a watershed method. Tests using the 3D data collected on 10th Street, Atlanta, Georgia and 6 mi of roadway on U.S. 80, Savannah, Georgia, has shown a 94.97% accuracy, 90.80% precision, and 98.75% recall. It has been demonstrated that the proposed method is promising for pothole detection and can provide a reliable method for pothole detection, especially when 3D pavement data have been collected for crack detection and already available.

Automatic pavement crack segmentation has gained attention among researchers and transportation agencies over the past two decades. However, most existing algorithms using two-dimensional (2D) pavement intensity images cannot provide a satisfactory performance. With the advent of sensing technology, three-dimensional (3D) line laser pavement imaging systems have become available. The objective of this paper is to propose an enhanced dynamic optimization algorithm employing the advantages of 3D pavement data to improve crack segmentation. The proposed algorithm consists of three major stages. First, a two-step Gaussian filter is applied to remove outliers from the collected laser data and rectify the profile in order to reduce the influence of cross-slope and ruts on crack segmentation. Then, a rough crack segmentation stage is conducted to adaptively identify the crack regions of interest. Finally, a bounding box and major orientation for each valid crack region of interest will provide searching ...