A Novel Optimization Technique for Detection of Concealed Blade in Mmw Imaging

Abstract

Millimeter-wave imaging is well suited for the detection of concealed weapons or other contraband carried on personnel, since millimeter waves are non-ionizing, readily penetrate common clothing material, and are reflected from the human body and any concealed items. The concealed weapon, like blade, detection and identification is one of the most puzzling task faces by security agency. Researchers have demonstrated MMW imaging systems to detect concealed targets like gun, knife and scissors but detection of small size target like blade with different orientation is still challenging due to resolution limitation of MMW imaging system. The success of small size concealed target detection depends upon scanning step size of imaging system and dielectric property of covering cloths and hidden object. Therefore, resolution enhancement techniques may play a very important role for small size concealed target detection. For this purpose, a critical analysis of various signal and image processing has been carried out and integrated following algorithms like singular value decomposition (SVD) for clutter reduction, discrete wavelet transform (DWT) for resolution enhancement, thresholding for target detection and in last artificial neural network (ANN) based algorithm for rotation invariant target identification. We implemented a novel integrated technique for blade detection from MMW images.