2025 Volume 6 Issue 1 Pages 312-322
Road managers typically conduct visual inspections and road surface condition surveys to assess pavement damage. However, there are challenges related to manpower shortages and cost constraints. To address these issues, pavement rut detection method using in-vehicle camera images and deep learning has been developed. The authors have also proposed a pavement rut detection approach that utilizes video images and image domain segmentation, successfully reducing false positives by considering the characteristic that pavement ruts occur in the longitudinal direction of the road. However, since the reflection of pavement ruts in video images is influenced by vehicle speed, and t assuming a constant speed, there remains a challenge of inadequate response to false detections caused by rough road surfaces and detection omissions.
In this research, correction method for rut detection that account for vehicle speed was developed. Demonstration experiments confirmed that this approach successfully addresses the issues identified in previous research.
