Simulated uav dataset for object detection

Abstract

Unmanned Aerial Vehicles (UAVs) have become increasingly popular for various applications, including object detection. Novel detector algorithms require large datasets to improve, as they are still evolving. Additionally, in countries with restrictive drone policies, simulated datasets can provide a cost-effective and efficient alternative to real-world datasets for researchers to develop and test their algorithms in a safe and controlled environment. To address this, we propose a simulated dataset for object detection through a Gazebo simulator that covers both indoor and outdoor environments. The dataset consists of 11,103 annotated frames with 27,412 annotations, of persons and cars as the objects of interest. This dataset can be used to evaluate detector proposals for object detection, providing a valuable resource for researchers in the field. The dataset is annotated using the Dark Label software, which is a popular tool for object annotation. Additionally, we assessed the dataset’s performance using advanced object detection systems, with YOLOv3 achieving 86.9 mAP50-95, YOLOv3-tiny achieving 79.5 mAP50-95, YOLOv5 achieving 82.2 mAP50-95, YOLOv7 achieving 61.8 mAP50-95 and YOLOv8 achieving 87.8 mAP50-95. Overall, this simulated dataset is a valuable resource for researchers working in the field of object detection.