Neural Network Application to the Postal Terahertz Scanner for Automated Detection of Concealed Items

Kovbasa, Mykola; Голенков, А. Г.; Сизов, Ф. Ф.

doi:10.1109/ukrmw49653.2020.9252706

Cited by 4 publications

(10 citation statements)

References 11 publications

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“…The dataset used in this paper is an improvement over a previously presented dataset, 13 which contained 1000 samples with 160×200 pixels images. The new dataset presented in this paper contains more than 1000 samples, and the images have a resolution of 160×820.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…In a previous study, 13 the use of object detection and instance segmentation techniques with a THz scanner were explored. To acquire images, an in-house THz scanner was utilized.…”

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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“…Nonetheless, standard security screening systems that rely on X-ray imaging and metal detectors are not foolproof, as they have a limited capacity to identify nonmetal threats, such as ceramic knives, plastic and powder explosives, liquids, and biological threats. 1,2 Unfortunately, parcel and mail screening is still entirely human, without the support of current automated technologies. 1,3 As a result, there is an urgent need for innovative solutions to develop fast and efficient screening systems that can overcome the limitations of traditional systems.…”

Section: Introductionmentioning

confidence: 99%

“…1,2 Unfortunately, parcel and mail screening is still entirely human, without the support of current automated technologies. 1,3 As a result, there is an urgent need for innovative solutions to develop fast and efficient screening systems that can overcome the limitations of traditional systems. This development involves two main aspects: upgrading the imaging hardware and developing computer-aided support.…”

Section: Introductionmentioning

confidence: 99%

“…Exploiting this advantage, active transmission sub-THz imaging scanners, particularly operating at 100 GHz, have been recently proposed for screening postal items. 1,3,[8][9][10] Automatic concealed objects detection and recognition represents a current challenge in developing modern sub-THz scanners for security screening. The recognition process typically encompasses preprocessing, detection, localization, classification, and tagging tasks.…”

Section: Introductionmentioning

confidence: 99%

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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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An optimal deep learning model for recognition of hidden hazardous weapons in terahertz and millimeter wave images

Jayachitra,

Manisekaran

et al. 2023

Earth Sci Inform

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Neural Network Application to the Postal Terahertz Scanner for Automated Detection of Concealed Items

Cited by 4 publications

References 11 publications

Study of object detection in linear terahertz imaging systems

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

An optimal deep learning model for recognition of hidden hazardous weapons in terahertz and millimeter wave images

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