CTX-UXO: A Comprehensive Dataset for Detection and Identification of UneXploded Ordnances

According to US NOAA, unexploded ordnances (UXO) are ”explosive weapons such as bombs, bullets, shells, grenades, mines, etc. that did not explode when they were employed and still pose a risk of detonation”. UXOs are among the most dangerous, threats to human life, environment and wildlife protection as well as economic development. The risks associated with UXOs do not discriminate based on age, gender, or occupation, posing a danger to anyone unfortunate enough to encounter them. Contrary to expectations, an UXO is more hazardous than new ordnance, as its arming or initiation mechanisms may be active or compromised. A mistake in correctly identifying ordnance can be fatal, which is why a decision support system can assist in making decisions under continuous stress, where lives are at risk. Recent advances in computer vision demonstrate that object detection and identification can be applied across multiple domains. However, until now, UXO detection has been limited by the lack of a representative, comprehensive dataset that provides robustness across different scenarios. UXOs are often found in altered, oxidized, semi-buried states in hard-to-reach environments.We thus propose the Contextual Vision for Unexploded Ordnances (CTX-UXO) dataset, which provides a collection of labeled UXO images in various visual contexts within the visible spectrum. The dataset encompasses old munitions in different stages, across multiple environments, angles, distances, and with various types of cameras. Additionally, replicas of munitions, faithfully replicating the characteristics of real ordnance, were used to diversify the dataset by relocating or arranging them in new positions and environments, and by removing certain ordnance components. This approach aims to create a dataset that is as varied and representative of real-world scenarios as possible. The dataset will be periodically updated with new types of UXO in different visual contexts.We hope that this dataset represents a useful resource for researchers and engineers working on supervised and semi-supervised object recognition projects, with particular emphasis on civil protection and emergency situation management applications.An article describing a preliminary use case for the CTX-UXO dataset and our proposed methodology is available here:[1] Craioveanu M., Stamatescu G., Detection and Identification of Unexploded Ordnance using a Two-Step Deep Learning Methodology, 32nd Mediterranean Conference on Control and Automation, MED 2024, June 11-14, Chania, Greece.We would like to thank the personnel of the National Romanian Inspectorate for Emergency Situations for their logistical support in the collection and dissemination of this dataset.

据美国国家海洋和大气管理局（US NOAA）定义，未爆炸弹药（unexploded ordnances, UXO）指“炸弹、子弹、炮弹、手榴弹、地雷等爆炸性武器，在投放后未能起爆，且仍存在引爆风险”。未爆炸弹药是对人类生命、生态环境与野生动物保护以及经济发展构成最严重威胁的危险源之一。其带来的风险不会因年龄、性别或职业而异，任何不幸遭遇它们的人都可能面临危险。与预期相悖的是，未爆炸弹药比全新弹药更具危险性，因为其引信或启动机制可能处于激活状态或已受损。对弹药的识别失误可能致命，这也是为何决策支持系统能够在持续承压、生命悬于一线的场景下辅助决策。
近年来计算机视觉（computer vision）技术的进展表明，目标检测（object detection）与目标识别（object identification）可应用于众多领域。然而迄今为止，未爆炸弹药检测技术的发展仍受限于缺乏具有代表性、全面性且能在不同场景下保持鲁棒性的数据集。未爆炸弹药常以变形、氧化、半掩埋的状态存在于难以抵达的环境中。
为此我们提出了未爆炸弹药场景视觉数据集（Contextual Vision for Unexploded Ordnances, CTX-UXO），该数据集收录了可见光光谱（visible spectrum）下不同视觉场景中的标注未爆炸弹药图像。数据集涵盖了不同状态、不同环境、不同拍摄角度与距离，且使用多种相机拍摄的老式弹药图像。此外，为丰富数据集的多样性，我们使用了忠实还原真实弹药特性的弹药复制品，通过将其放置在新的位置与环境中，或移除部分弹药组件来扩充数据集。该方案旨在构建尽可能贴近真实场景、具备丰富多样性与代表性的数据集。本数据集将定期更新，收录不同视觉场景下的新型未爆炸弹药样本。
我们期望本数据集能够为从事监督式（supervised）与半监督式（semi-supervised）目标识别研究的科研人员与工程师提供助力，尤其适用于民防与应急场景管理相关的应用。一篇介绍CTX-UXO数据集初步应用案例与我们提出的方法的论文可参见：[1] Craioveanu M., Stamatescu G., Detection and Identification of Unexploded Ordnance using a Two-Step Deep Learning Methodology, 第32届地中海控制与自动化会议（MED 2024），2024年6月11日至14日，希腊干尼亚。
我们谨向罗马尼亚国家应急事务监察局的工作人员致谢，感谢他们在本数据集的收集与传播过程中提供的后勤支持。

搜集汇总

背景与挑战

背景概述

CTX-UXO是一个用于未爆弹药（UXO）检测和识别的综合图像数据集，包含15,449个实例和3,520张JPG格式图像，覆盖多种弹药类型（如炮弹、迫击炮弹、手榴弹等）和视觉环境（如不同角度、距离和光照条件）。该数据集支持YOLO和COCO标注格式，适用于二进制分类、多类检测和实例分割等计算机视觉任务，旨在为研究和应急管理应用提供多样化的训练资源。