Shandilya Shishir Kumar, Srivastav Aditya, Yemets Kyrylo, Datta Agni, Nagar Atulya K
School of Computing Science and Engineering, VIT Bhopal University, India.
Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine.
Data Brief. 2023 Jun 27;50:109355. doi: 10.1016/j.dib.2023.109355. eCollection 2023 Oct.
The use of unmanned aerial vehicles (UAVs) has been rapidly increasing in both professional and recreational settings, leading to concerns about the safety and security of people and facilities. One area of research that has emerged in response to this concern is the development of detection systems for UAVs. However, many existing systems have limitations, such as detection failures or false detection of other aerial objects, including birds. To address this issue, the development of a standard dataset that provides images of both drones and birds is essential for training accurate and effective detection models. In this context, we present a dataset consisting of images of drones and birds operating in various environments. This dataset will serve as a valuable resource for researchers and developers working on UAV detection and classification systems. The dataset was created using Roboflow software, which enabled us to efficiently edit and manipulate the images using AI-assisted bounding boxes, polygons, and instance segmentation. The software supports a wide range of input and output formats, making it easy to import and export the dataset in different machine learning frameworks. To ensure the highest possible accuracy, we manually segmented each image from edge to edge, providing the YOLO model with detailed and accurate information for training. The dataset includes both training and testing sets, allowing for the evaluation of model performance and accuracy. Our dataset offers several advantages over existing datasets, including the inclusion of both drones and birds, which are commonly misclassified by detection systems. Additionally, the images in our dataset were collected in diverse environments, providing a wide range of scenarios for model training and testing. The presented dataset provides a valuable resource for researchers and developers working on UAV detection and classification systems. The inclusion of both drones and birds, as well as the diverse range of environments and scenarios, makes this dataset a unique and essential tool for training accurate and effective models. We hope that this dataset will contribute to the advancement of UAV detection and classification systems, improving safety and security in both professional and recreational settings.
无人驾驶飞行器（UAV）在专业和娱乐领域的使用都在迅速增加，这引发了对人员和设施安全保障的担忧。针对这一担忧出现的一个研究领域是无人机检测系统的开发。然而，许多现有系统存在局限性，比如检测失败或误将包括鸟类在内的其他空中物体检测为无人机。为解决这个问题，开发一个能提供无人机和鸟类图像的标准数据集对于训练准确有效的检测模型至关重要。在此背景下，我们展示了一个由在各种环境中运行的无人机和鸟类图像组成的数据集。这个数据集将成为致力于无人机检测和分类系统的研究人员和开发人员的宝贵资源。该数据集是使用Roboflow软件创建的，这使我们能够利用人工智能辅助的边界框、多边形和实例分割有效地编辑和处理图像。该软件支持多种输入和输出格式，便于在不同的机器学习框架中导入和导出数据集。为确保尽可能高的准确性，我们对每张图像进行了从边缘到边缘的手动分割，为YOLO模型提供详细准确的训练信息。该数据集包括训练集和测试集，可用于评估模型性能和准确性。我们的数据集相对于现有数据集具有多个优势，包括同时包含无人机和鸟类，而它们通常会被检测系统误分类。此外，我们数据集中的图像是在不同环境中收集的，为模型训练和测试提供了广泛的场景。所展示的数据集为致力于无人机检测和分类系统的研究人员和开发人员提供了宝贵资源。同时包含无人机和鸟类，以及多样的环境和场景，使得这个数据集成为训练准确有效模型的独特且必不可少的工具。我们希望这个数据集将有助于推动无人机检测和分类系统的发展，提高专业和娱乐领域的安全保障。
