Abstract:
In general, potholes on asphalt pavements can be detected and represented in 2D and 3D. However, pothole
detections through 3D imaging and image reconstructions have proven to be expensive in terms of acquisition
equipment and the computational and processing requirements and time. For potholes at incipient formations,
their detection, representation and quantification in terms of the surface-area are important for timely main-
tenance and repairs. By casting pavement image segmentation for pothole detection as a problem of clustering
multivariate features within mixed pixels (mixels), this study presents a low-cost 2D vision image-based approach
for the detection of potholes on asphalt road pavements in urban areas. The approach in this study is based on
the a priori integration of multiscale texture-based image filtering for textons representation using wavelet
transform, into the superpixel clustering of the pavement defects and non-defects using fuzzy c-means (FCM)
algorithm. For the extraction of the defects extrema (minima and maxima) in the hybrid wavelet-FCM clustering
results, fine segmentation based on morphological reconstruction is adopted to further smoothen and recognize
the contour of the detected potholes. The methodology is implemented in a MATLAB prototype, tested and
validated using 75 experimental image datasets. With a mean CPU run-time of 95 seconds, the average detection
accuracies by comparing the study results and the manually segmented ground-truth data were determined using
the Dice coefficient of similarity, Jaccard Index and sensitivity metric as 87.5%, 77.7% and 97.6% respectively.
The average magnitudes of the mean and standard deviation of the percentage errors in pothole size extractions
were detected as 8.5% and 4.9% respectively. The results of the study show that with well-planned road con-
dition surveys, the proposed algorithm is suitable for the detection and extraction of incipient potholes from 2D
vision images acquired using low-cost consumer-grade imaging sensors.
