Image Classification for Defect Detection of Light Aircraft

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Abstract
Visual inspections of aircraft are a vital part of routine procedures for maintenance personnel in the aviation industry. However, visual inspections take up a considerable amount of time to perform and are susceptible to human error. To mitigate this, utilising image classification for detecting defects is proposed. This approach utilises transfer learning of ResNet-50 within MATLAB to determine whether a defect is present in an image taken of the aircraft. The proposed method offers a solution for improving the efficiency and accuracy of defect detection during a general visual inspection in the aviation industry. Targeted defects here are damaged_skin and missing_rivets alongside a class denoting no_defect. Validation and testing accuracies achieved in this study are 88.33 % and 65.55 %, respectively.