Passive Mine Detection and Classification Method Based on Hybrid Model

Abstract

At present, active detectors are commonly used for detection of land mines. Land mines can be detected with high precision through active detectors. However, the operating principle of active detectors can also lead to vital dangers. When detecting mines in the field, electrical signals sent to the environment from active detectors sometimes trigger the mine blasting mechanism and cause mine explosion. Another way to detect land mines without triggering the blasting mechanisms is to use passive detectors. The biggest handicap of passive detectors is that they cannot detect mines as much as active detectors. This causes that passive detectors are as dangerous as at least active detectors. In this case, passive detectors can cause dangerous results like active detectors. In this paper, we have developed solutions that eliminate the handicaps of passive mine detectors. For this purpose, a new approach, which is established on artificial intelligence based on the magnetic anomaly, measurement height, and soil type, is suggested. The experimental setup is designed to verify and test the proposed approach. In this respect, the actual data measured under different conditions were recorded and processed with modern and effective artificial intelligence techniques; and alternative models were developed. With the proposed approach, the mines are detected with a success rate of 98.2%. This success rate in detection is promising for the passive mine detectors. A significant contribution of the developed model in terms of literature is the successful classification as well as the detection of mines. In experimental studies conducted with real data, five different types of mines are classified as 85.8% success rate. The proposed model has been a pioneering study on mine classification in the literature. Moreover, the realization of this paper with a passive mine detector proves the success of the proposed approach.

Keywords

• Mine detection and diagnosis
• meta-heuristic classification
• artificial neural network