Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods

Danso, Samuel Akwasi; Shang, Liping; Deng, Hu; Odoom, Justice; Liu, Quancheng; Nyarko, Benedicta Nana Esi

doi:10.3390/app12157354

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…The characterization of kernel size of the convolutional layers for THz deep learning models for high precision THz tomography was presented by Hung and Yang [117] and Transfer learning was demonstrated for automatic recognition of defects hidden in fiber reinforced polymer based THz nondestructive technology [118]. The security inspection based on deep learning and THz imaging technology is another application that has been leveraged to detect dangerous goods and hidden dangerous objects with accuracy and speed that meets the optimum security check requirements [119]- [123] as well as in industrial inspection THz applications for recognition of defects in integrated circuits (IC) [124], plastics and ceramics in real-time manufacturing process [125] [126] and nondestructive testing of impurities in wheat grains [127].…”

Section: B Classification Detection and Identificationmentioning

confidence: 99%

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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Following the recent progress in the development of Terahertz (THz) generation and detection, THz technology is being widely used to characterize test sample properties in various applications including nondestructive testing, security inspection and medical applications. In this paper, we have presented a broad review of the recent usage of artificial intelligence (AI) particularly, deep learning techniques in various THz sensing, imaging, and spectroscopic applications with emphasis on their implementation for medical imaging of cancerous cells. Initially, the fundamentals principles and techniques for THz generation and detection, imaging and spectroscopy are introduced. Subsequently, a brief overview of AI -machine learning and deep learning techniques is summarized, and their performance is compared. Further, the usage of deep learning algorithms in various THz applications is reported, with focus on metamaterials design and classification, detection, reconstruction, segmentation, parameter extraction and denoising tasks. Moreover, we also report the metrics used to evaluate the performance of deep learning models and finally, the existing research challenges in the application of deep learning in THz cancer imaging applications are identified and possible solutions are suggested through emerging trends. With the continuous increase of acquired THz data -sensing, spectral and imaging, artificial intelligence has emerged as a dominant paradigm for embedded data extraction, understanding, perception, decision making and analysis. Towards this end, the integration of state-of-the-art machine learning techniques such as deep learning with THz applications enable detailed computational and theoretical analysis for better validation and verification than modelling techniques that precede the era of machine learning. The study will facilitate the large-scale clinical applications of deep learning enabled THz imaging systems for the development of smart and connected next generation healthcare systems as well as provide a roadmap for future research direction.

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Section: B Classification Detection and Identificationmentioning

confidence: 99%

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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“…Currently, research 16 demonstrates using Yolov5 for object detection of hidden objects on the snapshots retrieved using the TeraSense facility. A novel method for detecting targets in terahertz images is proposed, utilizing a bi-directional feature pyramid network feature fusion approach.…”

Section: Previous Workmentioning

confidence: 99%

Study of object detection in linear terahertz imaging systems

Kovbasa,

Golenkov,

Shevchik-Shekera

et al. 2023

Opt. Eng.

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A neural network-based system designed for automated detection of concealed items using a postal terahertz scanner is presented, with system optimization provided. A dataset of objects scanned by a THz scanner is introduced. A convolutional neural network is trained on this dataset of terahertz images to classify and detect whether an image contains a prohibited item or not. The system is tested using realworld samples, achieving an accuracy of 95.5% mAP@0.5. The results demonstrate the effectiveness of employing a neural network in postal terahertz scanners and its potential for use in security and surveillance applications.

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“…Most related research works focused on concealed weapon detection under clothing for personal screening. In active transmission sub-THz imaging, transfer learning based CNNs using pretrained models, such ass YOLOv3, 6 YOLOv5, 29 YOLOv7, 30 RetinaNet, 16 region-based CNN (R-CNN), 2 and others, 1 , 9 , 31 have been proposed for concealed item recognition, particularly in sub-THz imaging scanners for postal screening applications.…”

Section: Introductionmentioning

confidence: 99%

Concealed objects detection and recognition in active transmission sub-terahertz imaging scanner for automatic security screening

Hocine,

Hamid,

Mohamed

2024

Opt. Eng.

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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.

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Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods

Cited by 11 publications

References 40 publications

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Study of object detection in linear terahertz imaging systems

Concealed objects detection and recognition in active transmission sub-terahertz imaging scanner for automatic security screening

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