Researchers at UC San Diego are using statistical pattern recognition and image processing to help the U.S. military better detect hidden roadside explosives.
Under a grant funded by the U.S. Department of Homeland Security through the National Science Foundation, UC San Diego structural engineering professor Francesco Lanza di Scalea is working on an imagery-based surveillance technique which uses visible and infrared images, analyzed by statistical pattern recognition algorithms to detect and classify suspicious objects such as camouflaged bombs placed at roadside and in airports. Lanza di Scalea is one of a handful of researchers in the United States who was awarded the one-time, three-year NSF grant.
The goal of the NSF program, called "Explosives and Related Threats: Frontiers in Prediction and Detection," is to advance fundamental knowledge in new technologies for sensors and sensor networks, and in the use of sensor data in control and decision making, particularly in relation to the prediction and detection of explosives and related threats. The NSF describes this research as critical to the nation's ability to deploy effective homeland security measures to protect civilians and U.S. military forces around the world.
"What we hope to do is use image processing and monitor different wavelengths of an object to detect a certain shape of an outside container, and to also determine whether it is empty, or if it has some metal inside," Lanza di Scalea said. "We are focusing on trying to detect or identify improvised explosive device (IED) camouflages such as cardboard boxes and cigarette cartons found in Iraq and Afghanistan."
According to the Defense Department, improvised explosive devices account for 50 percent of all daily attacks in Iraq. Of the three types of IEDs (roadside bombs, vehicle-born bombs and suicide bombs), roadside bombs are responsible for the most casualties. The most common IED camouflages in Iraq includ
|Contact: Andrea Siedsma|
University of California - San Diego