Efficient Detection of Landmines From Acoustic Images

Abstract

Abstract-The Laser Doppler Vibrometer (LDV)-based Acoustic toSeismic (A/S) landmine detection system is one of the reliable and powerful landmine detection systems. The interpretation of the LDVbased A/S data is performed off-line, manually, depending heavily on the skills, experience, alertness and consistency of a trained operator. This takes a long time. The manually obtained results suffer from errors, particularly when dealing with large volumes of data. This paper proposes some techniques for the automatic detection of objects from the acoustic images which are obtained from the LDV-based A/S landmine detection system. These techniques are based on Corresponding author: F. E. Abd El-Samie (fathi sayed@yahoo.com). 80Kasban et al.color image transformations, morphological image processing and the wavelet transform. The proposed techniques are compared considering the probability of detection, the false alarm rate, the accuracy and the processing speed.

Detection of Landmines from Acoustic Images Based on Cepstral Coefficients

“…Without preprocessing, the detection probability of landmines is about 90%. Morphological preprocessing can increase this detection probability to about 97% with a small false alarm probability but all these probabilities are in the absence of any type of noise [1,2]. The issue of noise effect is an issue which is rarely studied by researchers in this area.…”

“…These techniques begin by intensity thresholding of images to reject the dark background and then the detection of landmines based on their dimensions [1,2]. The drawbacks of these techniques lie in their inability to detect landmines with small dimensions in the images and their inability to reject background noise in the intensity thresholding approach.…”

“…They are classified as Antipersonnel (AP) landmines which are used to maim or kill persons and Antitank (AT) landmines which are used to attack vehicles and their occupants. There are about 100 million buried landmines covering more than 200,000 square kilometers of the world's surface and affecting about 70 countries [1,2]. Many obstacles are faced in removing these buried landmines, such as the loss or absence of maps or information about these mines or even the areas where they were laid in, the change of mine locations due to climatic and physical factors, the large variety of types of AP and AT landmines and the high costs needed to remove mines.…”

Resolution enhancement of images for further pattern recognition applications

“…According to the Federal Bureau of Investigations, it suffices to identify a fingerprint by matching 12 minutiae, but it has been reported that in most cases, 8 matched minutiae are enough. [15,16]. Many obstacles are faced in removing these buried landmines, such as the loss or absence of maps or information about these mines or even the areas, where they were laid in, the change of mine locations due to climatic and physical factors, the large variety of types of AP and AT landmines, and the high costs needed to remove mines.…”

“…These techniques begin by intensity thresholding of images to reject the dark background and then detection of landmines based on their dimensions [15,16]. The drawbacks of these techniques lie in their inability to detect landmines with small dimensions and their inability to reject background noise in the intensity thresholding approach.…”