Dangerous Items Dataset for 5-Class Object Detection (Pascal VOC annotation)

This repository contains the data from the manuscript "Dangerous Items Detection in Surveillance Camera Images Using Faster R-CNN". It contains 4,000 images representing 5 classes of objects: baseball bat, gun, knife, machete and rifle. All images were scaled so that the smaller side is no shorter than 600 pixels and the larger one is no longer than 1,000 pixels. The full set was randomly divided into training and testing parts (75% and 25% of the full set respectively). As a result, the training part contains 3,000 images, and the testing part – 1,000 images. Both parts are balanced, that is, they contain similar number of objects to be detected. Images were annotated using bounding boxes in the Pascal VOC format, according to which the description about each image is included in the corresponding XML file.

Using this dataset please cite:Omiotek, Z. (2025). Dangerous items’ detection in surveillance camera images using Faster R‑CNN. Przegląd Elektrotechniczny, 101(5), 156-168. https://www.red.pe.org.pl/articles/2025/5/36.pdf