Automatic road-distress classification and identification using a combination of hierarchical classifiers and expert systems-subimage and object processing

Automatic recognition of various road distresses is of considerable interest since it facilitates preventive road maintenance before cracks and potholes become too severe, leading to economic benefits. The current approach of using human operators to categorize road distresses is both labor-intensive and time consuming. We describe a two-step algorithm that automates road-distress identification with high accuracy. After constant-false-alarm-rate (CFAR) detection at the pixel level, subimage processing classifies each subimage of 64×64 pixels (each pixel is 1 mm by 1 mm) into crack, patch/pothole (P2), sealed crack, and false alarm. Object processing performs spatial clustering and object segmentation prior to final distress identification. The major challenge is integrating a number of signal and image processing algorithms to effectively deal with false alarms, film artifacts, and nonstationary distress characteristics and background. We explore how various signal and image processing concepts in signal projection, nonlinear filtering, feature optimization, image coding, and pattern recognition can be judiciously combined for computationally efficient and robust identification of road distresses. Our data analysis of 112 image frames (each frame contains 6144×4095 pixels) shows that the overall system performance at the object level is as follows: a P_D of 0.90 (average of 74 subimages per detected object), probability of correct distress identification of 0.96, and a P_FA of 0.79 false objects (average of 11 subimages per false object) per image frame