Bird/Drone Detection and Classification using Classical and Deep Learning Methods

Abstract

Machine learning techniques have always been a strong candidate for solving complex recognition problems. Drone/Bird detection and classification is one of the most challenging tasks in recent years. Both drones and birds come in different sizes, velocities, and behaviors. The lack of bird images and videos is tackled in this work. Deep learning, classical machine learning techniques such as Support Vector Machines (SVM) and Random Forest (RF) in addition to shallow neural network (NN) learning methods are used. Combined open-source data sets and labeled bird images data sets are used in training and testing for detection and classification. In particular, several deep learning methods are used in the detection of RGB and IR drone images. They were compared with the new SSD-AdderNet which showed the best results in the detection of IR images while exhibiting the least complexity. The SVM proved to be the best in classification.