Researchers Roger Achkar and Michel Owayjan at the American University of Science & Technology, Beirut, Lebanon have published the simulation results of a computer vision system they are developing that uses autonomous robots to detect landmines.
Landmines plague over 60 countries and where 80% of landmine victims were military personnel a century ago, today, 90% are civilians. While anti-personnel landmines are planted underground, anti-tank landmines are above ground and therefore visible to the computer vision system.
Using an autonomous robot equipped with computer vision overcomes several drawbacks of existing anti-tank minesweeping techniques.
Manual minesweeping where humans use metal detectors is dangerous, slow, and information capture to aid future efforts is, for all intents and purposes, non-existent.
Mechanized minesweeping is faster and eliminates the human safety concern; however, it cannot access the variety of locations humans can.
An autonomous robot with computer vision improves on mechanized minesweeping by using image processing and machine learning to continuously inform the next sweep.
The robot scans the terrain to see if a mine exists and uses a camera to capture the scanned area. Next, it processes the image digitally to minimize noise and bring out the features of the landmines.
Once images are enhanced, it accesses an Artificial Neural Network previously trained with images of known landmines. This enables the system to identify, recognize and classify them by make and model with a 90% success rate—even when the images captured show landmines rotated differently or partly hidden.
See the full article the September 2012 issue of the International Journal of Artificial Intelligence and Applications (IJAIA) here.
This blog is sponsored by ImageGraphicsVideo, a company offering ComputerVision Software Development Services.