Foreign Object Debris (FOD) Classification Through Material Recognition Using Deep Convolutional Neural Network With Focus on Metal

Abstract

Foreign object debris (FOD) is any undesired and unintended object placed or found in the specific vicinity of an aircraft (runway/ taxiway) that can cause damage to aircraft or harm personnel on board such as twisted metal strips, screws, nuts, and bolts, depleted concrete runway pieces, stones, pebbles and stationery items. To avoid FOD damages, all airport/ aviation organizations have deployed some sort of FOD prevention procedure. However, automatic FOD detection systems are still scarce owing to the inevitable reliance on human experts that lead to unavoidable human errors. Around 60% of FOD consists of metal which is the most deteriorating for an aircraft. Therefore, the implementation of material recognition techniques for FOD classification through Deep Convolutional Neural Networks (DCNN) is more important than FOD object detection as FOD could be of any shape, size or color. This paper developed a DCNN algorithm for FOD material classification with high accuracy for all included material classes (i.e., metal, concrete, plastic) in general and metal in particular. For this, a new dataset is introduced that consists of 2481 images taken on an operational airport runway in varying illumination and weather conditions. Through extensive testing, it was found that InceptionV3 is the best performing model with 18% improvement in metal recognition, and 11% improvement in average accuracy for all included classes.