Enhancement of Foreign Object Debris Material Recognition using Deep Learning and Machine Learning

Abstract

Foreign Object Debris (FOD) is considered one of the significant problems in the airline maintenance industry, reducing the levels of safety. A foreign object may result in causing severe damage to airplanes, including engine problems and personal safety risks. Therefore, it is critical to detect FOD in place to guarantee the safety of airplane flying. This paper proposes an FOD model using a variety of feature extraction approaches, including Convolution Neural Network (CNN) with VGG16, Linear Discriminant Analysis (LDA), and Gray-level Co-occurrence Matrix (GLCM) to extract FOD images' features. Moreover, two machine learning algorithms are used, namely Logistic Regression (LR) and Stochastic Gradient Descent (SGD), for classification purposes. The data for this research was taken from the Shanghai International Airport runways. The performance measures utilized in this paper are precision, accuracy, F-score, and recall. The experimental results obtained after implementation and testing the accuracy of 100%, the precision of 100%, recall of 100%, and F1-score of 100% for LR and SGD. The experiments demonstrate that the suggested technique has excellent detection accuracy. In addition, the suggested method should enable aircraft manufacturers to forecast the sort of FOD that will occur under specific conditions. In the proposed system, deep learning and machine learning methods will be utilized to deliver an accurate recognition result with as few FOD images as possible with a low error rate.