Critical Assessment of Pavement Distress Segmentation Methods

TLDR

Image segmentation is essential for automatic pavement distress detection and classification, yet full automation remains a challenge. The study aims to quantitatively evaluate the performance of six segmentation algorithms using an objective scoring measure. The authors reviewed current research, then tested the six methods on a diverse set of real pavement images from I‑75/I‑85 with varying lighting, shadows, and crack positions. The dynamic optimization‑based method outperforms the others, proving robust to image variations but requiring high computation time, and the analysis offers guidance for future algorithm development.

Abstract

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 radiography images, outperforms the other five methods based on our scoring measure. It is robust to image variations in our data set but the computation time required is high. By critically assessing the strengths and limitations of the existing algorithms, the paper provides valuable insight and guideline for future algorithm development that are important in automating image distress detection and classification.