Anti-UAV
A benchmark and toolkit for discovering, detecting, recognizing, and tracking UAVs in the wild using RGB and thermal infrared video.
What is Anti-UAV?
Anti-UAV is a research project and benchmark suite for detecting and tracking unmanned aerial vehicles (drones) in real-world environments. It provides datasets, evaluation metrics, and baseline models to address the computer vision task of discovering, detecting, recognizing, and tracking UAV targets using RGB and/or thermal infrared video. The project aims to mitigate security risks from unauthorized drone intrusions.
Computer vision researchers and engineers working on object detection, tracking, and aerial security systems, particularly those focused on multi-modal (RGB/IR) video analysis and real-world drone surveillance applications.
It offers the first high-quality, publicly available benchmark specifically for anti-UAV tasks, with large-scale datasets capturing challenging real-world scenarios. The dual support for PyTorch and Jittor frameworks provides flexibility and optimization for different hardware environments.
Overview
🔥🔥Official Repository for Anti-UAV🔥🔥
Use Cases
Best For
- Building computer vision models for drone detection and tracking in security applications
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