DETECTION ALGORITHM FOR DETECTING DRONES/UAVs

Unmanned Aerial Vehicles (UAVs), also popularly known as drones, have had an exponential evolution in recent times. This has resulted in better and affordable artifacts with applications in numerous fields. However, drones have also been used in terrorist acts, privacy violations and involuntary accidents in high risk zones. To address this problem, for our final year project we are working on studying and implementing various techniques and algorithms to automatically detect, identify and track small drones. We did a literature survey on the current deployed methodologies. Many state of the art techniques in recent times include Radio-Frequency, Audio-based and Radio-Frequency based methods. We mainly focused on video surveillance methods supported by computer vision algorithms. We used YOLOv5 architecture and implemented background subtraction methods within it. We modified the network to incorporate these methods. We further tested our model with the test dataset and compared the results with the benchmark models. We compared our results with the state of the art models based on visual data. We deployed our model to identify and locate drones and birds using a live camera in real time. We also tested a pruned version of our model to further improve the result. Further, we examined all the possible improvements and modifications that could be applied to our existing model to enhance the evaluation metrics.