CTX-UXO: Contextual visual dataset for localisation, classification and identification of unexploded ordnance

Unexploded Ordnances (UXOs) are explosive weapons that failed to detonate during deployment, posing significant threats to civilian safety and post-conflict recovery. The Contextual Vision for Unexploded Ordnances (CTX-UXO) dataset addresses this challenge by providing 3520 high-resolution RGB images, median size 2124 px square, containing UXOs collected over four years across all seasons and weather conditions. The dataset encompasses 15 449 annotated instances labeled as UXOs, with twelve subcategories for identification tasks, in which mortar bombs, projectiles and grenades constitute the predominant classes, supporting systems research and development in UXO classification, detection, instance segmentation and related applications, and providing multiple labelling formats such as YOLO and COCO. For the detection task including binary classification, an end-to-end solution for training, validation and testing is available as a reference by presenting a neural network optimized for edge deployment, Faster-RCNN with MobileNet V3 backbone, deployed for real-time inference with reduced costs on Nvidia Jetson Orin, achieving up to 23 frames per second with 62.18% Ap50 for UXO localisation and binary classification.