RESEARCH ON EVALUATE CRACKS IN ROAD PAVEMENT USING DRIVE RECORDER FOOTAGE BY DEEP LEARNING

Abstract

 The soundness of road pavements in our country is maintained by daily visual inspections as well as ro ad surface condition surveys once every several years conducted by road administrators. These inspection s and surveys require time-consuming work and expensive inspection costs, which are regarded as proble ms. To resolve the present situation, some methods have been proposed in recent years including a metho d for detecting road damage, such as cracks and potholes in the road pavement, using deep learning from i mages shot by a camera mounted on a traveling vehicle, and a method for calculating the percentage of cr acking. However, these methods are different from the rating method in actual inspections in some points.

 In this study, a method for rating cracking in the road pavement is developed by applying deep learning to video images from a drive recorder. It was found that the rating craking corresponding to the diagnostic categories I to III in the pavement inspection procedure can be carried out in consideration of cracks and patching by the devised method.