Foreign Object Debris Material Recognition based on Ensemble Learning Algorithm

Abstract

The material characteristics of foreign Object Debris (FOD) are the essential criteria in determining the extent of an aircraft's damage. Foreign object debris (FOD) can cause significant accidents and financial losses on airport runways. A new FOD material recognition strategy is proposed in this paper using an ensemble learning algorithm, namely KNN, Adaboost, and Random Forest Tree, to classify FOD images. In addition, this study uses different feature extraction methods like Linear Discriminant Analysis (LDA) and Gray-level co-occurrence matrix(GLCM) to extract FOD features. The KNN, Adaboost, and Random Forest Tree precision are 94.20%, 98.9%, and 99.7%, respectively. The dataset that was used has been collected by researchers from several datasets. As a result, the experiment results reveal that the proposed framework is effective and accurate. The results showed that the best classification machine algorithm is Random Forest Tree.