Drone Detection and Tracking with YOLO and a Rule-based Method
Purbaditya Bhattacharya, Patrick Nowak
TL;DR
This study addresses the need for automated drone detection in public spaces to protect privacy and safety by extending a multi-sensor dataset with annotated color images and additional infrared data. It trains YOLOv7-based detectors (initialized from COCO) on infrared and color subsets and investigates lightweight attention modules to boost accuracy, complemented by a simple rule-based cross-frame tracker to reduce detection drops. The results show strong single-image performance with reasonable inference times, and the rule-based tracker yields additional detections, improving robustness in video. The work demonstrates a practical drone monitoring pipeline that leverages multisensor data and a minimal tracking extension, with potential for live deployment after further model comparisons and multi-sensor calibration.
Abstract
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.