The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pengecaman objek ancaman dalam imej keselamatan sinar-x adalah salah satu aplikasi praktikal penting penglihatan komputer. Walau bagaimanapun, penyelidikan dalam bidang ini telah dihadkan oleh kekurangan set data yang tersedia yang akan mencerminkan tetapan praktikal untuk aplikasi tersebut. Dalam makalah ini, kami membentangkan pendekatan pengesanan anomali (GBAD) berasaskan GAN sebagai penyelesaian kepada masalah ketidakseimbangan kelas yang melampau dalam klasifikasi berbilang label. Kaedah ini membantu dalam menyekat lonjakan dalam positif palsu yang disebabkan oleh melatih CNN pada set data tidak praktikal. Kami menilai kaedah kami pada pangkalan data imej x-ray berskala besar untuk meniru rapat senario praktikal dalam sistem pemeriksaan keselamatan pelabuhan. Eksperimen menunjukkan peningkatan berbanding algoritma sedia ada.
Joanna Kazzandra DUMAGPI
Kwangwoon University
Woo-Young JUNG
Kwangwoon University
Yong-Jin JEONG
Kwangwoon University
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Salinan
Joanna Kazzandra DUMAGPI, Woo-Young JUNG, Yong-Jin JEONG, "A New GAN-Based Anomaly Detection (GBAD) Approach for Multi-Threat Object Classification on Large-Scale X-Ray Security Images" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 2, pp. 454-458, February 2020, doi: 10.1587/transinf.2019EDL8154.
Abstract: Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8154/_p
Salinan
@ARTICLE{e103-d_2_454,
author={Joanna Kazzandra DUMAGPI, Woo-Young JUNG, Yong-Jin JEONG, },
journal={IEICE TRANSACTIONS on Information},
title={A New GAN-Based Anomaly Detection (GBAD) Approach for Multi-Threat Object Classification on Large-Scale X-Ray Security Images},
year={2020},
volume={E103-D},
number={2},
pages={454-458},
abstract={Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.},
keywords={},
doi={10.1587/transinf.2019EDL8154},
ISSN={1745-1361},
month={February},}
Salinan
TY - JOUR
TI - A New GAN-Based Anomaly Detection (GBAD) Approach for Multi-Threat Object Classification on Large-Scale X-Ray Security Images
T2 - IEICE TRANSACTIONS on Information
SP - 454
EP - 458
AU - Joanna Kazzandra DUMAGPI
AU - Woo-Young JUNG
AU - Yong-Jin JEONG
PY - 2020
DO - 10.1587/transinf.2019EDL8154
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2020
AB - Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.
ER -