Deep learning–based drone detection with SWIR cameras

다국어 초록 (Multilingual Abstract)

Small unmanned aerial vehicles, commonly known as drones, and their related industries are improving in leaps and bounds. The global drone industry began with a military focus and subsequently progressed into commercial applications. Consequently, abuse cases linked to drone technology are gradually increasing. Following the technical advancement in drone technology, studies on drone detection and prevention are actively ongoing. This is one such study. Radar-based drone detection that combines various existing sensors or equipment has shortcomings, including high costs and specialist operations. Thus, this paper proposes a drone-detection system that uses only thermal images from short-wavelength infrared (SWIR) cameras. The YOLO model, which is widely used for object recognition, was used for the drone-detection algorithm. Labels were attached to 22,921 thermal images to test the constructed system; 16,121 images were used for training and the remainder for testing. The test results showed 98.17% precision and 98.65% recall. Learning through drone-image shooting in various environments, after removing static from clouds and other noise, is expected to improve detection performance in the future.

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