  One of the most difficult jobs in remote sensing is dealing with traffic
bottlenecks at airports. This fact has been confirmed by several studies
attempting to resolve this issue. Among a wide range of approaches employed the
most successful methods have been based on airplane object recognition using
satellite images as datasets for deep learning models. Airplane object
identification is not a viable method for resolving traffic congestion. There
are several types of airplanes at the airport each with its own set of
requirements and specifications.Utilizing satellite pictures will require the
use of complex equipment which is a financial burden. In this work a universal
low-cost and efficient solution for airport traffic congestion is proposed.
Drone-captured aerial pictures are used to train and assess a Mask Region
Convolution Neural Network model. This model can detect the presence of any
aircraft in a photograph and pinpoint its location. It also includes mask
estimations to properly identify each detected airplane type based on the
estimated surface area and cabin length which are crucial variables in
distinguishing planes. The study is conducted using Microsoft Common Object in
Context COCO metrics average accuracies and a confusion matrix all of which
demonstrate the approach's potential in providing valuable aid for dealing with
traffic congestion at airports.
