Development of Filtering Techniques for Multiple Features in 4K Vehicle-Mounted Camera Images to Enhance Crack Diagnosis of Road Pavement

Abstract

Road administrators typically conduct road condition surveys and visual inspections to assess the state of road pavements, but these methods are labor-intensive and costly. To address this issue, the authors developed a simple method for road pavement crack diagnosis using video images captured by a vehicle-mounted camera and deep learning techniques. However, the authors discovered that patching, manholes, and vehicles in front of the pavement were often misidentified as cracks. Therefore, in this study, the existing method was enhanced by incorporating a function to detect patching, manholes, and vehicles in advance, and a new approach was developed to diagnose cracks in road pavements by filtering out multiple geographical features. The results from the demonstration tests showed that the calculated cracking rate improved by approximately 5% compared to the existing method, confirming the effectiveness of the proposed method.