Automated detection and classification of concealed objects using infrared thermography and convolutional neural networks.

This paper presents a study on the effectiveness of a convolutional neural network (CNN) in classifying infrared images for security scanning. Infrared thermography was explored as a non-invasive security scanner for stand-off and walk-through concealed object detection. Heat generated by human subjects radiates off the clothing surface, allowing detection by an infrared camera. However, infrared lacks in penetration capability compared to longer electromagnetic waves, leading to less obvious visuals on the clothing surface. ResNet-50 was used as the CNN model to automate the classification process of thermal images. The ImageNet database was used to pre-train the model, which was further fine-tuned using infrared images obtained from experiments. Four image pre-processing approaches were explored, i.e., raw infrared image, subject cropped region-of-interest (ROI) image, K-means, and Fuzzy-c clustered images. All these approaches were evaluated using the receiver operating characteristic curve on an internal holdout set, with an area-under-the-curve of 0.8923, 0.9256, 0.9485, and 0.9669 for the raw image, ROI cropped, K-means, and Fuzzy-c models, respectively. The CNN models trained using various image pre-processing approaches suggest that the prediction performance can be improved by the removal of non-decision relevant information and the visual highlighting of features.

Material-Enabled Impact Detection and Damage Localisation System Using Shape Memory Alloy Tufted Composites.

Shape memory alloy (SMA) tufted composites have shown a significant improvement of the mechanical strength, fracture toughness, and delamination resistance of structural joints. This paper investigated the self-sensing functionality of SMA tufted carbon/epoxy composite T-joints to enable in situ strain monitoring for the detection of low-velocity impacts. Indeed, large deformations in the tufted composite due to impacts caused abrupt changes in electrical resistance of SMA filaments, which were used to trigger the detection system. An Arduino Mega controller was programmed to simultaneously extract and process real-time electrical resistance recordings from SMA tufts during impact tests conducted at 5 J and 10 J. Experimental results showed that the proposed SMA-enabled detection system can capture accurately the time of the impact and localise the delamination onset, thus demonstrating the truly multifunctional capabilities of proposed SMA tufted composites.