Chapter 10 (in Part 2): Detecting Landmine Fields from Low-Resolution Aerial Infrared Images
Book chapter, 2011

This chapter primarily addresses the problems of landmine field detection through the use of low-resolution infrared (IR) images captured from airborne or vehicle-borne passive IR cameras. We describe a scale-space-based scheme for detecting landmine candidates, as indications of landmine fields. The scheme contains two parts: In the first part, a multi-scale detector, using a special type of isotropic bandpass filters, is employed; In the second part, refinement of landmine candidates is performed through a post-processing scheme that seeks maximum consensus of corresponding landmine candidates over image frames. The chapter also briefly addresses the problems of landmine detection from high-resolution IR images measured at close distances to ground surfaces. A detector based on thermal contrast model is described. Experiments were conducted on several IR image sequences measured from airborne and vehicle-borne cameras, and on IR images measured at close distances to ground surfaces, where some results are included. Our experiments on these methods have shown that landmine signatures have significantly been enhanced after the processing, and automatic detection results are reasonably good. These methods may therefore be potentially employed for assisting humanitarian demining work.

airborne measurement

infrared landmine modeling

infrared images

Landmine field detection

multiscale detector

vehicle-borne measurement

isotropic bandpass filter

hypothesis test.

salient point feature detection

maximum consensus

Author

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Tardi Tjahjadi

The University of Warwick

Using Robots in Hazardous Environments: Landmine detection, de-mining and other applications

244-268

Areas of Advance

Information and Communication Technology

Subject Categories

Energy Engineering

Vehicle Engineering

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1533/9780857090201

ISBN

1845697863

More information

Created

10/8/2017