14 March 2024 Concealed objects detection and recognition in active transmission sub-terahertz imaging scanner for automatic security screening
Medjadba Hocine, Zehaf Hamid, Lazoul Mohamed
Author Affiliations +
Abstract

As part of developing a sub-terahertz imaging scanner for automatic security screening applications, we investigate the detection and recognition of concealed objects in sub-terahertz images using various image processing techniques capable of addressing the low quality of such images. First, we propose a method that combines locally adaptive and multilevel thresholding with connected components labeling. This method enables the removal of the nonuniform background, facilitating the detection and localization of multiple concealed objects. Subsequently, we analyze the performance of several local feature-based matching methods for selecting the optimal detector–descriptor combination, allowing for efficient detection and recognition of concealed objects. Furthermore, we assess the applicability and performance of the supervised machine learning method based on the support vector machine classifier combined with the bag of visual words model. Finally, we explore the effectiveness of transfer learning by fine-tuning two pretrained convolutional neural networks, namely AlexNet and YOLOv3 models. The two fine-tuned models achieve recognition accuracies of 87% and 92% for AlexNet and YOLOv3, respectively. Benchmarking the study results demonstrates that deep learning methods provide an efficient solution for automatic security screening systems.

© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE)
Medjadba Hocine, Zehaf Hamid, and Lazoul Mohamed "Concealed objects detection and recognition in active transmission sub-terahertz imaging scanner for automatic security screening," Optical Engineering 63(3), 033102 (14 March 2024). https://doi.org/10.1117/1.OE.63.3.033102
Received: 8 September 2023; Accepted: 19 February 2024; Published: 14 March 2024
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KEYWORDS
Object detection

Image segmentation

Detection and tracking algorithms

Education and training

Image classification

Sensors

Machine learning

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