Research on a portable automatic aircraft surface damage detection technology

Automated aircraft surface damage detection faces numerous issues in engineering practice. This paper presents a portable automatic aircraft surface damage detection technology based on machine vision. It analyzes the difficulties associated with aircraft surface inspection using machine vision and optimizes the classification of aircraft surface damage according to machine vision requirements, thus laying a foundation for improving detection effectiveness. YOLOv8 was selected as the basic algorithm model and Jetson Nano as the implementation platform for the design and development of the portable automatic aircraft surface damage detection algorithm. The relevant experimental environment was established to train and validate the algorithm model. Experimental results show that the portable automatic aircraft surface damage detection method developed in this paper achieves good detection performance, with the mAP of 69.53% and GFLOPs of 7.48. This technology can serve as an auxiliary technical means in the field of aircraft manufacturing or maintenance, effectively enhancing the automation level of aircraft surface inspections.