UETT4K Anti-UAV: A Large Scale 4K Benchmark Dataset for Vision-Based Drone Detection in High-Resolution Imagery

Abstract

In recent years, incidents involving drones have increased significantly, raising concerns over security and privacy, especially concerning civilian and military facilities. Vision-based approaches, especially those employing deep convolutional neural networks (DCNNs), show great promise in addressing the need for an accurate and cost-effective drone detection system. However, DCNNs rely heavily on extensive and well-labeled datasets, which are essential for achieving high accuracy and effectiveness. For drone detection tasks, a dataset of high-resolution images is especially valuable, as it provides more contextual information for DCNNs, enabling more accurate drone detection. This work presents a new drone detection dataset composed of 4K resolution images named University of Engineering and Technology Taxila 4K Anti-UAV (UETT4K Anti-UAV). The dataset is created by obtaining real-world videos of different types of drones in diverse environmental and challenging conditions. A custom dataset of 33601 images, manually annotated with hand-labeled bounding boxes, is created from these videos. We used our dataset to train, validate, and test eight state-of-the-art YOLOv6v3 algorithm models. The test results and qualitative analysis guide selecting the most suitable model for drone detection applications. The proposed extensive 4K dataset offers a valuable resource for effectively training deep learning models to detect drones across diverse conditions accurately.