Abstract:
In recent years, the issue of foreign object debris at airports has grown significantly. It
is noted that airport runways, entryways, and taxiways are the primary locations where
foreign object debris-related incidents happen [2]. Foreign object debris can damage
the tires or engines of the aircraft, rendering them inoperable. The current foreign
object debris detection solutions are limited by their reliance on outdated datasets. To
tackle this problem an updated local dataset is used and this dataset was obtained
by capturing images through the mobile camera on the airport surface. The dataset
consists of more than 30,000 images and 3 categories of foreign objects including concrete (stones), metal (nuts, bolts, nails, etc.), and plastic (bottles, caps, pens, etc.).
The proposed framework for identifying foreign object debris integrates augmentation
techniques to overcome the difficulties presented by the datasets’ different light levels, noise, and blurry images. Roboflow is used to annotate the images of this dataset.
Within this context, the current research introduces an inventive methodology designed
to address the issue of foreign object detection, and the newly released You Only Look
Once (YOLOv8) with a transfer learning technique. An object detector’s performance
is usually assessed, considering inference time and detection accuracy. When compared
to two-stage object detectors, single-stage detectors often obtain higher detection accuracy. Optimized parameters are used after the implementation of hyperparameter
tuning. The inference time is 4.2ms for 25 epochs. The proposed YOLOv8 model has
achieved mean Average Precision (mAP50) of 96.5% and (mAP50-95) of 74.5% for all
classes and they improved after using the transfer learning approach. However, a high
mAP50 score of 0.975 for the class "metal" in the pre-trained model indicates that the
model successfully identifies the instances of metal in the images.
