Enhanced visualisation of concealed target objects by infrared thermography and machine learning

Abstract

Electromagnetic waves such as millimetre-wave, terahertz, and infrared have been extensively employed in security scanning applications due to their non-invasive nature and strong detection capabilities. Millimetre-wave and terahertz imaging systems benefit from longer wavelengths, allowing for deeper penetration through clothing. This has enabled the development of commercial stand-off and walk-through detectors for the identification of concealed items beneath garments. In contrast, infrared radiation, while less penetrative and yielding weaker signals on clothing surfaces, offers significantly higher imaging resolution due to its shorter wavelength. Additionally, infrared cameras are generally more cost-effective, widely available, and well-suited for high-throughput applications over longer distances. In this work, a machine learning-based methodology was applied to thermal infrared images to improve the detection and visualisation of objects concealed under layered clothing. Principal Component Analysis was employed to identify pixels with marked thermal contrast between the subject and the background. This was followed by image segmentation using the Chan-Vese active contour algorithm and region-of-interest clustering using Fuzzy-c means. Finally, image fusion with the corresponding visible spectrum image was performed to enhance the interpretability of the results. Compared to K-means clustering, the proposed Fuzzy-c-based approach demonstrated improved performance in localising concealed objects, such as eliminating low heat signal due to the palm’s residual heat, underscoring the potential of machine learning-enhanced infrared imaging for practical security applications.