DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING

Abstract

The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained model was re-trained on custom drone synthetic dataset by using transfer learning’s fine-tune technique. The results of detecting drone in our experiments were around 90.8%. The combination of drone detection, Dlib correlation tracking algorithm and centroid tracking algorithm effectively detects and tracks the small drone in various complex environments as well as is able to handle multiple target appearances.