Early Detection of Small and Medium-Sized Drones in Complex Environments

• Published in: Drone systems and applications no. ja
• Main Author: Last, Mark
• Format: Journal Article
• Language: English
• Published: Canadian Science Publishing 19.08.2025
• ISSN: 2564-4939; 2564-4939
• DOI: 10.1139/dsa-2025-0018

Unmanned Aerial Vehicles (UAVs), or drones, have become a weapon of choice on the modern battlefield. As military and civilian industries try to develop effective counter-drone systems, early detection of flying drones still poses multiple challenges to state-of-the-art computer vision technologies. These challenges include a growing variety of military and commercial drones, their small size compared to piloted aircraft, blending with the background, similarity to birds, etc. In our experiments on drone images of variable size, we have observed a rapid drop in the accuracy of a state-of-the-art drone detection model when applied to distant drones that take a relatively small area on the entire image. However, we show that this early detection accuracy can be significantly improved by applying the drone detection model to an image masked by the Canny edge detector. We suggest applying the model to the original and the masked images in parallel and determining a drone detection decision by the highest confidence value under the condition that the detected object is not recognized as a bird by a general-purpose object detection model. The results of our evaluation experiments confirm the effectiveness of the proposed drone detection approach.