Drone Detection and Tracking with YOLO and a Rule-based Method

TL;DR

This paper presents a drone detection system combining YOLOv7-based deep learning models with a rule-based tracker to improve detection and tracking of drones in videos, addressing privacy and safety concerns.

Contribution

It extends an existing drone image dataset, trains YOLOv7 models for drone detection, and introduces a simple cross-correlation tracker to enhance video tracking performance.

Findings

YOLOv7 models achieve high detection accuracy on the extended dataset.

The cross-correlation tracker reduces detection drops in video sequences.

The integrated system effectively detects and tracks drones in real-time videos.

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.