Design of airport road surface inspection system based on machine vision and deep learning

Abstract

The specific objective of this study was to solve the problem of high risk and low efficiency of manual inspection of airport road surface. This paper describes the design of an Airport Road Surface Intelligent Inspection System Based on machine vision and deep learning. The system completes the airport pavement inspection through autonomous navigation. MobileNet-SSD and Mask R-CNN algorithms are used for target detection and semantic segmentation of airport pavement apparent disease and foreign object bebris in this paper. At the same time, topological feature extraction and pixel size measurement were carried out. Finally, the information is uploaded to the storage server and the target information is displayed on the Android mobile terminal. The inspection system can complete the work of airport pavement inspection and provide a new way for airport pavement health monitoring.