A Lower Complexity Deep Learning Method for Drones Detection

Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities can deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep-learning techniques to benchmark real data sets of flying drones. A Deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the SSD paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning techniques such as SVM is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were either RGB or IR data. Comparisons were made between all these types and conclusions are presented.