Optimizing Yolov3 Detection Model Using Terahertz Active Security Scanned Low-Resolution Images.

Danso, Samuel Akwasi; Shang, Liping; Deng, Hu; Odoom, Justice; Chen, Linyu; Xiong, Zhong-gong

doi:10.15863/tas.2021.03.95.39

Cited by 2 publications

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“…However, the main challenge stems from the low quality of sub-THz images, posing difficulties for effective detection and recognition of concealed objects. 6 In fact, sub-THz images typically exhibit all characteristics of low-count images, 7 such as low contrast and low signal-to-noise ratio, resulting in fuzziness in the image.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the advantages of the sub-THz radiation, developing modern sub-THz screening systems requires the automation of the detection and recognition tasks. However, the main challenge stems from the low quality of sub-THz images, posing difficulties for effective detection and recognition of concealed objects 6 . In fact, sub-THz images typically exhibit all characteristics of low-count images, 7 such as low contrast and low signal-to-noise ratio, resulting in fuzziness in the image.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods

et al. 2022

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As a harmless detection method, terahertz has become a new trend in security detection. However, there are inherent problems such as the low quality of the images collected by terahertz equipment and the insufficient detection accuracy of dangerous goods. This work advances BiFPN at the neck of YOLOv5 of the deep learning model as a mechanism to improve low resolution. We also perform transfer learning, thereby fine-tuning the pre-training weight of the backbone for migration learning in our model. Results from experimental analysis reveal that mAP@0.5 and mAP@0.5:0.95 values witness a percentage increase of 0.2% and 1.7%, respectively, attesting to the superiority of the proposed model to YOLOv5, which is the state-of-the-art model in object detection.

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Optimizing Yolov3 Detection Model Using Terahertz Active Security Scanned Low-Resolution Images.

Cited by 2 publications

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

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

Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods

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