Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security Applications

X-ray baggage inspection has been widely used for maintaining airport and transportation security. Towards automated inspection, recent deep learning-based methods have attempted to detect hazardous objects directly from X-ray images. Since it is challenging to collect a large number of training ima...

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Main Authors: Hyo-Young Kim, Sung-Jin Cho, Seung-Jin Baek, Seung-Won Jung, Sung-Jea Ko
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9552004/
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author Hyo-Young Kim
Sung-Jin Cho
Seung-Jin Baek
Seung-Won Jung
Sung-Jea Ko
author_facet Hyo-Young Kim
Sung-Jin Cho
Seung-Jin Baek
Seung-Won Jung
Sung-Jea Ko
author_sort Hyo-Young Kim
collection DOAJ
description X-ray baggage inspection has been widely used for maintaining airport and transportation security. Towards automated inspection, recent deep learning-based methods have attempted to detect hazardous objects directly from X-ray images. Since it is challenging to collect a large number of training images from real-world environments, most previous learning-based methods rely on image synthesis for training data generation. However, these methods randomly combine foreground and background images, restricting the effectiveness of synthetic images for object detection. To solve this problem, in this paper, we propose a learning-based X-ray image synthesis method for object detection. Specifically, for each foreground object to be synthesized, we first estimate positions difficult to detect by the object detector. These positions and their corresponding confidence values are then used to construct a difficulty map, which is used for sampling the target foreground position for image synthesis. The performance analysis using various state-of-the-art object detectors shows that the proposed synthesis method can produce more useful training data compared with the conventional random synthesis method.
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spelling doaj.art-75acd3ce38d64cf392da9c4c4b87d1932022-12-21T21:31:59ZengIEEEIEEE Access2169-35362021-01-01913525613526510.1109/ACCESS.2021.31162559552004Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security ApplicationsHyo-Young Kim0https://orcid.org/0000-0001-8498-9650Sung-Jin Cho1https://orcid.org/0000-0001-9910-419XSeung-Jin Baek2https://orcid.org/0000-0003-0494-2372Seung-Won Jung3https://orcid.org/0000-0002-0319-4467Sung-Jea Ko4https://orcid.org/0000-0002-4875-7091School of Electrical Engineering, Korea University, Seoul, South KoreaSchool of Electrical Engineering, Korea University, Seoul, South KoreaSchool of Electrical Engineering, Korea University, Seoul, South KoreaSchool of Electrical Engineering, Korea University, Seoul, South KoreaSchool of Electrical Engineering, Korea University, Seoul, South KoreaX-ray baggage inspection has been widely used for maintaining airport and transportation security. Towards automated inspection, recent deep learning-based methods have attempted to detect hazardous objects directly from X-ray images. Since it is challenging to collect a large number of training images from real-world environments, most previous learning-based methods rely on image synthesis for training data generation. However, these methods randomly combine foreground and background images, restricting the effectiveness of synthetic images for object detection. To solve this problem, in this paper, we propose a learning-based X-ray image synthesis method for object detection. Specifically, for each foreground object to be synthesized, we first estimate positions difficult to detect by the object detector. These positions and their corresponding confidence values are then used to construct a difficulty map, which is used for sampling the target foreground position for image synthesis. The performance analysis using various state-of-the-art object detectors shows that the proposed synthesis method can produce more useful training data compared with the conventional random synthesis method.https://ieeexplore.ieee.org/document/9552004/Deep learningneural networkobject detectionX-rayinspection
spellingShingle Hyo-Young Kim
Sung-Jin Cho
Seung-Jin Baek
Seung-Won Jung
Sung-Jea Ko
Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security Applications
IEEE Access
Deep learning
neural network
object detection
X-ray
inspection
title Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security Applications
title_full Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security Applications
title_fullStr Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security Applications
title_full_unstemmed Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security Applications
title_short Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security Applications
title_sort learning based image synthesis for hazardous object detection in x ray security applications
topic Deep learning
neural network
object detection
X-ray
inspection
url https://ieeexplore.ieee.org/document/9552004/
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AT seungjinbaek learningbasedimagesynthesisforhazardousobjectdetectioninxraysecurityapplications
AT seungwonjung learningbasedimagesynthesisforhazardousobjectdetectioninxraysecurityapplications
AT sungjeako learningbasedimagesynthesisforhazardousobjectdetectioninxraysecurityapplications