: The Threat Detection Group (TOG) at Defense R&D Canada-Suffield has undertaken a research program on the feasibility of remote sensing of minefields. One of the projects is the Remote Minefield Detection (RMD) hierarchical algorithm originally developed for single band active airborne infrared imagery (AAII) and later adapted for vehicle mounted passive infrared imagery (FLIR) The objectives of this contract are: (i) to Implement the RMD hierarchical algorithm on a down-scaled version of transputer architecture for real-time mine detection on FLIR Imagery, (ii) to develop the high and top levels of the algorithm Including expert system and knowledge base, and (iii) to research the possibility to upgrade the current hardware platform to modern advanced computational elements Phase 1 and 2 of the contract focused on the implementation of Low and Middle levels of a real-time RMD system on a transputer network to detect mines in real FLIR images. The work first described the hardware requirement for each module of the algorithm, then outlined the software development, and finally presented some preliminary test results. Although transputers were attractive computing elements 15 years ago when this project started, they have become obsolete quickly today Thus a decision has been made by the Project Authority to implement the RMD hierarchical algorithm on the new hardware platform, namely a network of Intel Pentium-class processors, which was recommended by a study from the University of British Columbia. Phase 3 of the contract Involved building that PC network, and developing and testing the new software version.

A pipelined algorithm and parallel architecture is under development for real time detection of landmines. Our previous work has dealt with monochromatic images from airborne active infrared scanners and images from a low-altitude aircraft-mounted multi-spectral scanner. Because of the nature of the sensors and the aerial observation platform, the landmines were treated as small, sparse, discrete objects in a large clutter field. Our current work deals with passive infrared imagery obtained from cameras mounted on ground vehicle. In contrast to the previous work, although the targets are still relatively sparse, they are no longer small in the sense of occupying just a few pixels and the signal to noise ratio is considerably worse than in for the airborne active infrared and multi-spectral scanner problems. So significant changes to our detection algorithm are needed. The paper describes the overall algorithm and the particular issues, such as irregular shapes, that need to be dealt with in FLIR imagery. Some early results are presented. In addition, changes in computer processing power and interprocessor communications has led to a rethink of the real-time hardware implementations of the system and these issues are discussed.

A pipe-lined algorithm and parallel architecture is under development for real time detection of sparse small objects in images. Monochromatic images from an airborne active infrared scanner, images from a low-altitude aircraft-mounted multispectral scanner, and passive infrared imagery obtained from cameras mounted on ground vehicle are the image types intended for the application of this system to the detection of minefields. The paper briefly describes the characteristics of these three different kinds of image sensors and the operating environments. The general image processing system architecture and the functions of each of the components are also presented The feature selection and algorithm adaptations for each of the image classes are described. Preliminary results obtained from an experimental system consisting of a small transputer network and array processors are discussed.