Visual monitoring of landing gear in fighters using deep learning

The analysis of images using deep learning techniques makes it possible to detect anomalous or dangerous situations in different fields of application. This work aims to ensure the correct configuration of landing gear during aircraft landings. In contrast with other works, the small object detection problem is solved using background subtraction technique, and subsequently feeding it to our proposed convolutional neural network to automatically classify the position of the landing gear. This work also develops a new database that combines synthetic and real images, generated from exclusive fighter landing manoeuvres performed by a real test pilot. The obtained model, trained with synthetic data and tested with real images, presents a 0.9981 of accuracy. The result is a functional system, tested against real images and endowed with “early warning” capability as it is able to detect the position of an aircraft’s landing gear in advance and prevent catastrophic accidents.