Drone Detection and Tracking with YOLO and a Rule-based Method
This work addresses the problem of detecting illegal drone activities for privacy and safety, but it is incremental as it builds on prior work and uses existing methods.
The paper tackles drone detection and tracking by training YOLOv7 and its variants on an extended open-source dataset, achieving results that are reported but not specified numerically, and uses a cross-correlation tracker to improve video performance.
Drones or unmanned aerial vehicles are traditionally used for military missions, warfare, and espionage. However, the usage of drones has significantly increased due to multiple industrial applications involving security and inspection, transportation, research purposes, and recreational drone flying. Such an increased volume of drone activity in public spaces requires regulatory actions for purposes of privacy protection and safety. Hence, detection of illegal drone activities such as boundary encroachment becomes a necessity. Such detection tasks are usually automated and performed by deep learning models which are trained on annotated image datasets. This paper builds on a previous work and extends an already published open source dataset. A description and analysis of the entire dataset is provided. The dataset is used to train the YOLOv7 deep learning model and some of its minor variants and the results are provided. Since the detection models are based on a single image input, a simple cross-correlation based tracker is used to reduce detection drops and improve tracking performance in videos. Finally, the entire drone detection system is summarized.