This work is concerned with buried landmines detection by long wave infrared images obtained during the
heating or cooling of the soil and a segmentation process of the images. The segmentation process is performed by
means of a local fractal dimension analysis (LFD) as a feature descriptor. We use two different LFD estimators,
box-counting dimension (BC), and differential box counting dimension (DBC). These features are computed in
a per pixel basis, and the set of features is clusterized by means of the K-means method. This segmentation
technique produces outstanding results, with low computational cost.
A great development of technologies for the detection of buried landmines took place worldwide in the last years. In
Argentina, a project for the development of an autonomous robot with sensors for landmines detection was recently
approved by the Science and Technology National Agency. Within this project we are studying the detection of
landmines by infrared radiation.
Metallic and plastic objects with landmines shape and dimension were buried at different depths from 1 to 4 cm in soil
and sand. Periodic natural warming by solar radiation or artificial warming by means of electric resistances or flash
lamps were applied. Infrared images were obtained in the 8-12 micrometers spectral band with a microbolometer
camera. The IR images were processed by different methods to obtain a definition as good as possible of the buried
objects. After this a B-Spline method was applied to detect the targets contours and determine shape and dimensions of
them so as to distinguish landmines from other objects.
We are looking for a landmine detection method as simple and fast possible, with detection capability of metallic and
plastic landmines and an acceptable false alarm rate which would be reduced when applied with other detection
methods as GPR and electromagnetic induction.
We present obtained and processed images and results obtained to distinguish buried landmines from other buried objects.
Presently, the number of landmines planted around the world totalizes more than 110 million and, far from slowing down,
the landmine production planting rate is, at least, one order of magnitude higher than the rate at which they are removed.
In this work a technique to detect buried landmines using boundary detection in IR images, is presented. The buried
objects have different temperature than the surrounding soil. We find the object contours by means of an algorithm of
B-Spline deformable curves.
Under a statistical model, regions with different temperatures can be characterized by the values of the statistical
parameters of these distributions. Therefore, this information can be used to find boundaries among different regions in the
The B-Spline approach has been widely used in curve representation for boundary detection, shape approximation,
object tracking and contour detection. Contours formulated by means of B-Splines allow local control, require few parameters
and are intrinsically smooth. The algorithm consists in estimating the parameters along lines strategically disposed
on the image. The true boundary is found when the values of these parameters vary abruptly on both sides. A likelihood
function is maximized to determine the position of such boundaries.
We present the experimental results, which show the behavior of the detection method, according to the buried object
depth and the elapsed time from the cooling initial time. The obtained results exhibit that it is possible to recognize the
shape of the objects, buried at different depths, with a low computational effort.
A great development of technologies for the detection of buried objects took place in the last years. Applications in archeology, finding of pipe lines and others were important, but most attention was paid in humanitarian detection of land mines and unexploded ordnances. Among these technologies, thermography is one of the most useful techniques and has been applied concurrent with other ones (Ground Penetrating Radar, Electromagnetic Induction, etc.) We have made several experiments to obtain thermographic images of buried objects in the middle and far infrared, in laboratory and in field, and in different types of terrain: naked ground, ground covered with grass and sand. We employed, as warming methods, natural sun radiation and blowing of warm air or halogen lamps. We have used metallic and dielectric objects of different sizes and shapes so as to recognize them by their characteristics. The acquired images were improved using noise reduction and image enhancement techniques.
In this work we present the thermographic images obtained. All measurements were made at short distance, less than 100 cm, as the objective of our work is to develop a thermographic imaging system for the detection of buried objects to be installed in an autonomous ground robot.