A feature‐based pavement image registration method for precise pavement deterioration monitoring

Over the past decade, pavement imaging systems, particularly 3D laser technology, have been widely adopted by transportation agencies for network‐level pavement condition evaluations. State Highway Agencies, including Georgia Department of Transportation (DOT), Florida DOT, and Texas DOT, have been collecting pavement images for over 5 years. However, these multi‐year pavement images have not been fully utilized for analyzing detailed pavement deterioration. One challenge is the accurate and efficient registration of multi‐temporal pavement images. This study pioneers the use of feature‐based methods to address this challenge. It evaluates various feature‐based image registration methods, including both state‐of‐the‐art and novel combinations of feature detectors and descriptors. These methods are rigorously assessed using hybrid “step‐by‐step” and “end‐to‐end” performance evaluation metrics, with a ground reference dataset containing 100 pavement image pairs featuring diverse crack types and varying year gaps. The results confirm the feasibility of using feature‐based techniques to register multi‐temporal pavement images. A novel combination of the AKAZE detector and the Binary Robust Independent Elementary Features (BRIEF) descriptor was identified as the best‐performing method, successfully registering 96 out of 100 image pairs. This advancement enables pavement engineers to accurately monitor pavement deterioration using multi‐temporal images.