TCDLN: Terahertz Colormap Deep Learning Network for Hidden Object Detection

Abstract

This paper is based on the recognition of hidden objects which is embedded in colormaps generated by using special programs in terahertz video surveillance systems. Terahertz imaging is popular because of its capability to see through opaque objects.  So, this imaging technique can be used in the detection of hidden objects, medical diagnosis, and many other real-life applications. This paper has two significant contributions: firstly, how to detect hidden objects in colormaps, and secondly to diagnose which colormap gives the best detection results. We shall involve a deep learning framework that utilizes Resnet-50, to detect hidden objects concealed within the color maps of terahertz images. Secondly, we have proposed a method for inspecting purple, grey, inverted cool, and grey+skeleton colormaps using a deep learning framework and concluded which colormap detects the hidden object most robustly.