Airplane Type Identification Based on Mask RCNN; An Approach to Reduce Airport Traffic Congestion

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. Utilising 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's Common Object in Context (COCO) metrics, average accuracies and a confusion matrix.