Public Affairs, Public Policy and Public Administration Commons^™

Computer Vision Detection Of Explosive Ordnance: A High-Performance 9n235/9n210 Cluster Submunition Detector, Adam Harvey, Emile Lebrun

The Journal of Conventional Weapons Destruction

The detection of explosive ordnance (EO) objects is experiencing a period of innovation driven by the convergence of new technologies including artificial intelligence (AI) and machine learning, open-source intelligence (OSINT) processing, and remote mobility capabilities such as drones and robotics.1 Advances are being made on at least two tracks: in the automated searching of photographic image archives, and in the real-time detection of objects in the field.2 Different technologies are responsive to different types of EO detection challenges, such as objects that are buried, semi-buried, or partially damaged. Computer vision—a type of artificial intelligence (AI) that enables computers and systems …

The Journal Of Conventional Weapons Destruction Issue 27.2 (2023), The Journal Of Conventional Weapons Destruction

The Journal of Conventional Weapons Destruction

• Updates on recent enhancements to IMAS.
• Food security and its connection to mine action as it applies to Ukraine.
• Digital EORE as a small NGO in mine action.
• A case study on moving beyond "do no harm" in environmental mainstreaming in mine action.
• Efforts of JICA and CMAC in fostering South-South cooperation in mine action.
• UAV Lidar imaging in mine action to detect and map minefields in Angola.
• Land disputes and rights in mine action.
• Computer vision detection of explosive ordnance.