Non-contact, portable, and stand-off infrared thermal imager for security scanning applications

Abstract

In this article, we demonstrated the physical application of a portable infrared (IR) security scanning system for the non-contact and stand-off detection of target objects concealed underneath clothing. Such a system combines IR imaging and transfer learning with convolutional neural networks (CNNs) to enhance the detection of weak thermal signals and automate the classification of IR images. A mid-wavelength IR detector was used to record the real-time heat emitted from the clothing surface by human subjects. Concealed objects reduce the transmissivity of IR radiation from the body to the clothing surface, generally showing lower IR intensity compared to regions without objects. Due to limited resources for training data, the transfer learning approach was applied by fine-tuning a pre-trained CNN ResNet-50 model using the ImageNet database. Two image types were investigated here, i.e., raw thermal and Fuzzy-c clustered images. Receiver operating characteristic curves were built using a holdout set, showing an area-under-the-curve of 0.8934 and 0.9681 for the raw and Fuzzy-c clustered image models, respectively. The gradient-weighted class activation mapping visualization method was used to improve target identification, showing an accurate prediction of the object area. It was also found that complex clothing, such as those composed of materials of different transmissivity, could mislead the model in classification. The proposed IR-based detector has shown potential as a non-contact, stand-off security scanning system that can be deployed in diverse locations and ensure the safety of civilians.