This paper gives a comparison of two vehicle-mounted infrared systems for landmine detection. The first system is a downward looking standard infrared camera using processing methods developed within the EU project LOTUS. The second system is using a forward-looking polarimetric infrared camera. Feature-based classification is used for this system. With these systems data have been acquired simultaneously of different test lanes from a moving platform. The performance of each system is evaluated using a leave-one-out method. On the training set the polarimetric infrared system performs better especially for low false alarm rates. On the independent evaluation set the differences are much smaller. On the ferruginous soil test lane the down-ward looking system performs better at certain points whereas on the grass test lane the forward-looking system performs better at certain points.
To acquire detection performance required for an operational system for the detection of anti-personnel Iandmines, it is necessary to use multiple sensors and sensor-fusion techniques. This paper describes five decision-level sensor-fusion techniques and their common optimisation method.The performance of the sensor-fusion techniques is evaluated by means of Receiver Operator Characteristics curves. These techniques are tested on an outdoor test facility. Three of four test lanes of this facility are used as training set and the fourth is used as evaluation set.The detection performance of naive Bayes, Dempster-Shafer, voting and linear discriminant are very similar on both the training and the evaluation set. This is probably caused by the flexibility of the sensor-fusion techniques resulting into similar optimal solutions independent of the fusion technique.The first LOTUS trial took place from May 17th through 21st 1999 at the TNO test site, situated in the dunes of the Hague, the Netherlands. This test site consists of 6 test lanes, each with a different soil type: sand, clay, peat, ferruginous, forest and road. Each test lane measures 3 by 1 0 m2 and has a controlled water table. The test lanes are free of vegetation and the soilof the first four test lanes is filtered to remove metal objects. In the test lanes, a mixture of anti-personal (AP) mines and anti-tank (AT) mines are buried or laid on the surface. The sensors are attached to a metal free X-Y table for taking stand-off measurements.In Table 1 an overview is given of the mine types in the six test lanes. In total there are 265mines consisting of 216 APs and 49 ATs. The mine types AP5 and AT3 are metal free. The others (73%) contain at least some metal, see Table 2. The first four test lanes contain roughly similar number of mines of each type. However, there are more mines present of mine type AP5 and AP7 on the first four test lanes.
A camera or display usually has a smaller dynamic range than the human eye. For this reason, objects that can be detected by the naked eye may not be visible in recorded images. Lighting is here an important factor; improper local lighting impairs visibility of details or even entire objects. When a human is observing a scene with different kinds of lighting, such as shadows, he will need to see details in both the dark and light parts of the scene. For grey value images such as IR imagery, algorithms have been developed in which the local contrast of the image is enhanced using local adaptive techniques. In this paper, we present how such algorithms can be adapted so that details in color images are enhanced while color information is retained. We propose to apply the contrast enhancement on color images by applying a grey value contrast enhancement algorithm to the luminance channel of the color signal. The color coordinates of the signal will remain the same. Care is taken that the saturation change is not too high. Gamut mapping is performed so that the output can be displayed on a monitor. The proposed technique can for instance be used by operators monitoring movements of people in order to detect suspicious behavior. To do this effectively, specific individuals should both be easy to recognize and track. This requires optimal local contrast, and is sometimes much helped by color when tracking a person with colored clothes. In such applications, enhanced local contrast in color images leads to more effective monitoring.
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