An IR mine detection system has been developed which reliably detects buried land miens in certain environmental conditions. The system uses two commercial IR cameras, one using the 3-5 micron band and the other using the 8-12 micron band. The cameras are mounted above a HMMWV and are tilted down to look 2.5 to 7 meters ahead of the vehicle, covering almost a four meter wide swath. Software algorithms are used to de-warp the raw images to remove keystoning and lens curvature effects, producing rectilinear images of the terrain. The mines are observed in the images as cool spots or hot spots on the surface of the soil, with temperatures depending upon the mine type, the recent temperature history of the soil, and the moisture content and soil type. Raw video images are presented which show some of these effects, including when the contrast is so low to be visibly hidden in the background. Filtering algorithms are utilized to perform background identification and removal, producing an equalized image, which enhances the contrast of the buried mines. Statistical order filters are employed that further enhance the mines, with examples again shown. Threshold and object detection algorithms have been developed that autonomously detect mine-like objects in the images without operator intervention. Feature extraction algorithms then search for features that distinguish the mines sought, including such features as size and shape. The objects are classified as a mine or a non-mine and this decision passed on to the registration and hi-level inference detection subsystems of the mine detection platform.