YOLO-based Segmented Dataset for Drone vs. Bird Detection for Deep and Machine Learning Algorithms

Unmanned aerial vehicles (UAVs) have become increasingly popular in recent years for both commercial and recreational purposes. Regrettably, the security of people and infrastructure is also clearly threatened by this increased demand. To address the current security challenge, much research has been carried out and several innovations have been made. Many faults still exist, however, including type or range detection failures and the mistaken identification of other airborne objects (for example, birds). A standard dataset that contains photos of drones and birds and on which the model might be trained for greater accuracy is needed to conduct experiments in this field. The supplied dataset is crucial since it will help train the model, giving it the ability to learn more accurately and make better decisions. The dataset that is being presented is comprised of a diverse range of images of birds and drones in motion. Pexel website's images and videos have been used to construct the dataset. Images were obtained from the frames of the recordings that were acquired, after which they were segmented and augmented with a range of circumstances. This would improve the machine-learning model's detection accuracy while increasing dataset training. The dataset has been formatted according to the YOLOv7 PyTorch specification. The test, train, and valid folders are contained within the given dataset. These folders each feature a plaintext file that corresponds to an associated image. Relevant metadata regarding the discovered object is described in the plaintext file. Images and labels are the two subfolders that constitute the folders. The collection consists of 20,925 images of birds and drones. The images have a 640 x 640 pixel resolution and are stored in JPEG format.

近年来，商用及民用无人机（Unmanned Aerial Vehicles, UAVs）的普及度与日俱增。遗憾的是，此类需求的增长也对民众安全与基础设施安全构成了显著威胁。为应对当前的安全挑战，学界已开展大量研究并取得多项创新成果，但仍存在诸多不足，例如目标类型识别失败、检测范围受限，以及将其他空中物体（如鸟类）误识别为无人机等问题。为此，亟需构建一套包含无人机与鸟类图像的标准数据集，用于模型训练以提升识别精度，从而开展该领域的相关实验。本数据集具备极高的应用价值，可助力模型训练，使模型实现更精准的学习与更合理的决策。本次发布的数据集包含多样化的运动状态下的无人机与鸟类图像，素材来源于Pexels网站的图像与视频：通过提取录制视频的帧并进行分割、多场景数据增强处理后构建而成，此举可提升机器学习模型的检测精度，同时优化数据集的训练效果。本数据集已按照YOLOv7 PyTorch规范完成格式适配。数据集包含训练（train）、验证（valid）与测试（test）三个文件夹，每个文件夹下均设有图像子文件夹（images）与标签子文件夹（labels），每张图像对应一个同名纯文本标签文件，文件中记录了目标对象的相关元数据信息。本数据集共包含20925张鸟类与无人机图像，图像分辨率为640×640像素，采用JPEG（Joint Photographic Experts Group）格式存储。