drone-detection
seraphim-drone-detection-dataset
Seraphim Drone Detection Dataset
Dataset Overview
This is a comprehensive drone image dataset curated from 23 open-source datasets and processed through a custom cleaning pipeline. The dataset is designed for training object detection models to identify drones in various environments and conditions. The majority of images feature rotary-wing (multi-rotor) unmanned aerial vehicles (UAVs), with a smaller portion representing fixed-wing and hybrid.… See the full description on the dataset page: https://huggingface.co/datasets/lgrzybowski/seraphim-drone-detection-dataset.drone-audio-detection-samples
Dataset Description
Drone Audio Detection Samples (DADS) is currently the largest publicly available drone audio database, specifically designed for developing drone detection systems using deep learning techniques. All audio files are standardized to a sample rate of 16,000 Hz, 16-bit depth, mono-channel, and vary in length from 500 milliseconds to several minutes.
Most drone audio files were manually trimmed to ensure that a drone was always present in the recording. However, some… See the full description on the dataset page: https://huggingface.co/datasets/geronimobasso/drone-audio-detection-samples.Drone-Detectiondrone-detectionDrone_Detection
Dataset Card for Dataset Name
Credit: https://www.kaggle.com/datasets/dasmehdixtr/drone-dataset-uavThis is a dataset from the above the link. It's used for object detection training on yolo model for the class of drone.
Dataset Details
Dataset Description
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License: [More… See the full description on the dataset page: https://huggingface.co/datasets/ywanny/Drone_Detection.Drone_Detection_Survaillance
Drone Detection Surveillance
This dataset supports detecting small aerial objects in surveillance-style CCTV imagery. It includes labeled images for five classes: propeller drones, fixed-wing drones, helicopters, birds, and planes, to reduce confusion between drones and other objects.
The dataset is about 12 GB with roughly 15200 images. It’s organized into Train, Dev, and Test splits, then REAL and AI subfolders, and finally per-class folders. Annotations are Pascal VOC-style… See the full description on the dataset page: https://huggingface.co/datasets/ratio1/Drone_Detection_Survaillance.