Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection

Custom inspection using X-ray imaging is a very promising application of modern pattern recognition technology. However, the lack of data or renewal of tariff items makes the application of such technology difficult. In this paper, we present a data augmentation technique based on a new image-to-ima...

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Autores principales:	Hyunwoo Cho, Haesol Park, Ig-Jae Kim, Junghyun Cho
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	backscatter X-ray data augmentation cargo inspection generative adversarial network image translation Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/35adbe6a39f94563a90fff1916f4e633
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id	oai:doaj.org-article:35adbe6a39f94563a90fff1916f4e633
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spelling	oai:doaj.org-article:35adbe6a39f94563a90fff1916f4e6332021-11-11T19:14:55ZData Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection10.3390/s212172941424-8220https://doaj.org/article/35adbe6a39f94563a90fff1916f4e6332021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7294https://doaj.org/toc/1424-8220Custom inspection using X-ray imaging is a very promising application of modern pattern recognition technology. However, the lack of data or renewal of tariff items makes the application of such technology difficult. In this paper, we present a data augmentation technique based on a new image-to-image translation method to deal with these difficulties. Unlike the conventional methods that convert a semantic label image into a realistic image, the proposed method takes a texture map with a special modification as an additional input of a generative adversarial network to reproduce domain-specific characteristics, such as background clutter or sensor-specific noise patterns. The proposed method was validated by applying it to backscatter X-ray (BSX) vehicle data augmentation. The Fréchet inception distance (FID) of the result indicates the visual quality of the translated image was significantly improved from the baseline when the texture parameters were used. Additionally, in terms of data augmentation, the experimental results of classification, segmentation, and detection show that the use of the translated image data, along with the real data consistently, improved the performance of the trained models. Our findings show that detailed depiction of the texture in translated images is crucial for data augmentation. Considering the comparatively few studies that have examined custom inspections of container scale goods, such as cars, we believe that this study will facilitate research on the automation of container screening, and the security of aviation and ports.Hyunwoo ChoHaesol ParkIg-Jae KimJunghyun ChoMDPI AGarticlebackscatter X-raydata augmentationcargo inspectiongenerative adversarial networkimage translationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7294, p 7294 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	backscatter X-ray data augmentation cargo inspection generative adversarial network image translation Chemical technology TP1-1185
spellingShingle	backscatter X-ray data augmentation cargo inspection generative adversarial network image translation Chemical technology TP1-1185 Hyunwoo Cho Haesol Park Ig-Jae Kim Junghyun Cho Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection
description	Custom inspection using X-ray imaging is a very promising application of modern pattern recognition technology. However, the lack of data or renewal of tariff items makes the application of such technology difficult. In this paper, we present a data augmentation technique based on a new image-to-image translation method to deal with these difficulties. Unlike the conventional methods that convert a semantic label image into a realistic image, the proposed method takes a texture map with a special modification as an additional input of a generative adversarial network to reproduce domain-specific characteristics, such as background clutter or sensor-specific noise patterns. The proposed method was validated by applying it to backscatter X-ray (BSX) vehicle data augmentation. The Fréchet inception distance (FID) of the result indicates the visual quality of the translated image was significantly improved from the baseline when the texture parameters were used. Additionally, in terms of data augmentation, the experimental results of classification, segmentation, and detection show that the use of the translated image data, along with the real data consistently, improved the performance of the trained models. Our findings show that detailed depiction of the texture in translated images is crucial for data augmentation. Considering the comparatively few studies that have examined custom inspections of container scale goods, such as cars, we believe that this study will facilitate research on the automation of container screening, and the security of aviation and ports.
format	article
author	Hyunwoo Cho Haesol Park Ig-Jae Kim Junghyun Cho
author_facet	Hyunwoo Cho Haesol Park Ig-Jae Kim Junghyun Cho
author_sort	Hyunwoo Cho
title	Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection
title_short	Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection
title_full	Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection
title_fullStr	Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection
title_full_unstemmed	Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection
title_sort	data augmentation of backscatter x-ray images for deep learning-based automatic cargo inspection
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/35adbe6a39f94563a90fff1916f4e633
work_keys_str_mv	AT hyunwoocho dataaugmentationofbackscatterxrayimagesfordeeplearningbasedautomaticcargoinspection AT haesolpark dataaugmentationofbackscatterxrayimagesfordeeplearningbasedautomaticcargoinspection AT igjaekim dataaugmentationofbackscatterxrayimagesfordeeplearningbasedautomaticcargoinspection AT junghyuncho dataaugmentationofbackscatterxrayimagesfordeeplearningbasedautomaticcargoinspection
_version_	1718431576445943808

Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection

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