Landmine Recognition AI supports specialists in battlefields by detecting landmines and other unexploded ordnance.

May 8, 2024
Reading time
2 min read
Landmine Recognition: AI supports specialists in battlefields by detecting landmines and other unexploded ordnance.

An AI system is scouring battlefields for landmines and other unexploded ordnance, enabling specialists to defuse them.

What’s new: The military hardware firm Safe Pro Group developed Spotlight AI, a computer vision system that identifies mines based on aerial imagery, IEEE Spectrum reported. Nongovernmental organizations that remove landmines, including the Norwegian People's Aid and the HALO Trust, are using the system in Ukraine. 

How it works: SpotlightAI processes visual-light imagery taken by flying drones. The system provides centimeter-resolution maps that guide mine-removal teams through the territory.

  • The system includes an unidentified vision model trained to recognize 150 types of explosive munitions, primarily of U.S. and Russian origin. In a test, the model detected 87 percent of munitions scattered across a munitions test range in Hungary.
  • With sufficient computational resources, the system can analyze an image in around 0.5 seconds. A human reviewer typically takes three minutes.
  • The system struggles to identify explosives concealed by earth or dense vegetation. To address this limitation, Safe Pro Group has begun to test it with infrared, lidar, magnetometry, and other types of imagery. In addition, the company has developed a system that converts drone imagery into a heat map that shows a machine learning model’s estimated probability that it can detect explosives in a given location. A patch of grass, for example, may have a higher estimated probability than a dense thicket of trees and bushes.
  • The company aims to fine-tune its model to detect unexploded ordnance in other current or former conflict zones such as Angola, Iraq, and Laos.

Behind the news: In addition to drones, satellites can help machine learning models to find deadly remnants of warfare. In 2020, Ohio State University researchers estimated the number of undetonated explosives in Cambodia by collating bomb craters in satellite images identified by a computer vision model with records of U.S. military bombing campaigns in that country in the 1960s and 1970s.

Why it matters: Unexploded mines, bombs, and other types of munitions killed or injured more than 4,700 people — 85 percent of them civilians and half of them children where military status and age were known — in 2022 alone. Efforts to remove every last mine from a former battlefield likely will continue to rely on traditional methods — manual analysis of overhead imagery along with sweeps by human specialists and explosive-sniffing dogs — but machine learning can significantly reduce the hazard and accelerate the work.

We’re thinking: Although this system locates unexploded mines and shells, removing them often still falls to a brave human. We hope for speedy progress in robots that can take on this work as well.


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