Article: A sensor fusion model for the detection and classification of anti-personnel mines Journal: International Journal of Innovative Computing and Applications (IJICA) 2009 Vol.2 No.1 pp.45 - 59 Abstract: The ground penetrating radar (GPR) and infrared (IR) imaging have become two established sensors for detecting buried anti-personnel mines (APM) which contains no or a little metal. This paper introduces the GPR and IR techniques briefly and describes particular situations where each technique is feasible. We discuss the GPR and IR data acquisition, signal processing and image processing methods. This paper discusses the strengths and weaknesses of each of the sensors based on data capturing efficiency, overcoming environmental difficulties and sensor technology. By providing comparison of these two sensors, we emphasise the necessity of fusion to harness the advantages of each of the methods. We propose a geometrical feature-based sensor fusion framework, combining GPR and IR, as an effective technique for detection and classification of APM which reduces the false alarm rate significantly. We consider the basic geometrical shape descriptor features of an object and construct a feature vector for each of the objects. These feature vectors are used to train a probabilistic neural network (PNN) for the classification of APMs. The method gives almost perfect detection accuracy. Inderscience Publishers - linking academia, business and industry through research

Title: A sensor fusion model for the detection and classification of anti-personnel mines

Authors: Baikunth Nath, Alauddin Bhuiyan

Addresses: Department of Computer Science and Software Engineering, The University of Melbourne, Carlton VIC 3010, Australia. ' Centre for Eye Research Australia, The University of Melbourne, East Melbourne, VIC 3002, Australia

Abstract: The ground penetrating radar (GPR) and infrared (IR) imaging have become two established sensors for detecting buried anti-personnel mines (APM) which contains no or a little metal. This paper introduces the GPR and IR techniques briefly and describes particular situations where each technique is feasible. We discuss the GPR and IR data acquisition, signal processing and image processing methods. This paper discusses the strengths and weaknesses of each of the sensors based on data capturing efficiency, overcoming environmental difficulties and sensor technology. By providing comparison of these two sensors, we emphasise the necessity of fusion to harness the advantages of each of the methods. We propose a geometrical feature-based sensor fusion framework, combining GPR and IR, as an effective technique for detection and classification of APM which reduces the false alarm rate significantly. We consider the basic geometrical shape descriptor features of an object and construct a feature vector for each of the objects. These feature vectors are used to train a probabilistic neural network (PNN) for the classification of APMs. The method gives almost perfect detection accuracy.

Keywords: anti-personnel mines; APM detection; ground penetrating radar; GPR; infrared imaging; sensor fusion; neural networks; buried mines; data acquisition; signal processing; image processing; detection accuracy.

DOI: 10.1504/IJICA.2009.027996

International Journal of Innovative Computing and Applications, 2009 Vol.2 No.1, pp.45 - 59

Received: 02 Jun 2009
Accepted: 02 Jun 2009
Published online: 26 Aug 2009 *

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Title: A sensor fusion model for the detection and classification of anti-personnel mines

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