Pavement Crack Detection with Image Processing and Analysis

To enhance the accuracy of pavement crack detection, we implement a multi-stage image processing approach. First, we convert the pavement surface image to grayscale using RGB-to-grayscale conversion algorithms (typically weighted methods like luminance calculation: Gray = 0.299*R + 0.587*G + 0.114*B) to simplify intensity-based analysis. Next, we apply noise reduction filters—commonly Gaussian blur or median filters—to eliminate speckles and environmental interference that could impair detection accuracy.

For contrast enhancement, we employ histogram equalization techniques that redistribute pixel intensities to improve crack visibility. After preprocessing, we classify cracks using morphological operations and pattern recognition algorithms. Linear cracks are identified through edge detection algorithms (like Canny or Sobel) combined with Hough transform for line segment identification, while mesh-like cracks are detected using connected component analysis and texture pattern recognition.

Quantitative measurements involve calculating linear crack lengths using pixel connectivity and Euclidean distance measurements, and mesh crack areas through pixel counting within bounded regions. These metrics provide objective assessment of pavement deterioration severity, enabling maintenance prioritization based on structural damage extent.