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.