Shandilya, Shishir Kumar and Srivastav, Aditya and Yemets, Kyrylo and Datta, Agni and Nagar, Atulya K. (2023) YOLO-based Segmented Dataset for Drone vs. Bird Detection for Deep and Machine Learning Algorithms. Data in Brief. ISSN 2352-3409
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Abstract
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 valu- able 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 im- age 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 advant- ages 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.
| Item Type: | Article |
|---|---|
| Additional Information and Comments: | “NOTICE: this is the author’s version of a work that was accepted for publication in Data in Brief. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Data in Brief, available from: https://www.sciencedirect.com/science/article/pii/S2352340923004742 |
| Keywords: | Computer Vision; Drones Vs Birds; Drone Detection; Deep Learning; Machine Learning; Image Segmentation; Drone Security |
| Faculty / Department: | Faculty of Human and Digital Sciences > Mathematics and Computer Science |
| Depositing User: | Atulya Nagar |
| Date Deposited: | 17 Jul 2023 08:29 |
| Last Modified: | 17 Jul 2023 08:29 |
| URI: | https://hira.hope.ac.uk/id/eprint/3988 |
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