csv, and xml format) corresponding to the 3 scenario image folders. Each label folder contains 3 scenario annotation files (stored in. Each image folder is made up of 3 scenario folders (representing cloudy, sunny,and evening) containing the image files stored in. The visioDECT dataset is arranged in 6 folders (representing the 6 drone models) with each folder having 2 sub-folders (representing the images folders and labels folders). To ensure consistency in naming convention and minimize error, each scenario sub-class label files are named to correspond to their image files and stored in repositories accordingly. Data annotation was carried out by trained experts on each scenario sub-class in 3 file formats (txt, xml, and csv) by manually drawing bounding boxes around each image file to generate corresponding label files. To minimize error, trained professionals carried out data cleaning on each repository by manually eliciting image frames without corresponding drones at the background. Using reputable software applications, each video sequence is converted to JPEG image frames of 852 x 480 pixels and stored in repositories representing each model class and scenario sub-class. The video sequence of each scenario is recorded. Each drone model was flown at different altitudes and distances at different times of day, week, and month. The materials used for the data capturing includes drone models ( Anafi-Extended, DJI FPV, DJI Phantom, EFT-E410S, Mavic2-Air, and Mavic2-Enterprise), drone controllers, mobile phone with controller application, high-definition digital cameras, and tripod stands. The dataset consists of 20924 sample images and annotations from 6 drone models across 3 scenarios (cloudy, sunny, and evening), at different altitudes and distances (30m-100m), and in 3 different file formats (txt, xml, csv) that was generated at 12 different locations within a period of 1 year, 8 months by a team of domain experts. VisioDECT is a robust vision-based drone dataset for classifying, detecting, and countering unauthorized drone deployment using visual and electro-optical infra-red detection technologies. Consequently, there is a scarcity of robust datasets for the development of real-time systems that can checkmate the incessant deployment of UAVs in carrying out criminal or terrorist activities. The deployment of unmanned aerial vehicles (UAV) for logistics and other civil purposes is consistently disrupting airspace security.
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