1 research outputs found
Passive Mine Detection and Classification Method Based on Hybrid Model
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