Estimation of landmine characteristics in sandy desert using neural networks

Many places in the world are heavily contaminated with landmines, which cause that many resources are not utilized. This makes landmine detection and removal challenges for research. To guarantee reliable landmine sensing system, deep analysis and many test cases are required. The proposed concept is based on application of 1 kPa external constant pressure (lower than the landmine activation pressure) to the sand surface. The resultant contact pressure distribution is dependent on the imbedded object characteristics (type and depth). Then neural networks (NN) are trained to find the inverse solution of the sand–landmine problem. In other words, when the contact pressure is known, NN can estimate the imbedded object type and depth. In this work, using finite element modeling, the existence of landmines in sand is modeled and analyzed. The resultant contact pressure distribution for five objects (1—anti-tank, 2—anti-personnel, 3—can with diameter and height of 200 mm, 4—spherical rock with 200 mm diameter, and 5—sand without any object) in sand at different depths is used in training NN. Three NN are developed to estimate the landmine characteristics. The first one is perceptron type which classifies the introduced objects in sand. The other two feed-forward NN (FFNN) are developed to estimate the depth of two landmine types. The NN detection rates of anti-tank and anti-personnel landmines are 100 and 67 % in training, and 95 and 70 % in validation, respectively. As test cases, the detection rates of the NN in case of landmine inclination angles (0°–30°) are studied. The results show same detection rates as those at no inclination. A random noise 10 % of the average signal does not affect NN detection rates, which are the same as 95 and 70 % as in validation for anti-tank and anti-personnel, respectively, while with 20 % noise detection rates are decreases to 90 and 50 % for anti-tank and anti-personnel, respectively.