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
픽셀 수준 접근 방식과 같은 세분화된 이미지 분석은 X-Ray 보안 이미지의 위협 탐지를 향상시킵니다. 실제 환경에서는 완전한 픽셀 수준 주석을 얻는 데 드는 비용이 크게 증가하며, 이는 데이터 세트에 부분적으로 레이블을 지정하여 줄일 수 있습니다. 그러나 부분적으로 레이블이 지정된 데이터세트를 처리하면 복잡한 다단계 네트워크를 훈련하게 될 수 있습니다. 본 논문에서는 부분적으로 레이블이 지정된 데이터 세트에서 단일 네트워크를 훈련하는 동시에 데이터 및 객체 제안 수준에서 고유한 클래스 불균형을 완화하는 새로운 엔드 투 엔드 객체 분리 프레임워크를 제안합니다. 경험적 결과는 기존 접근 방식에 비해 상당한 개선이 있음을 보여줍니다.
Joanna Kazzandra DUMAGPI
Kwangwoon University
Yong-Jin JEONG
Kwangwoon University
객체 분리, 위협 탐지, 엑스레이 수하물 보안, 깊은 학습, 분할
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Joanna Kazzandra DUMAGPI, Yong-Jin JEONG, "End-to-End Object Separation for Threat Detection in Large-Scale X-Ray Security Images" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 10, pp. 1807-1811, October 2022, doi: 10.1587/transinf.2022EDL8019.
Abstract: Fine-grained image analysis, such as pixel-level approaches, improves threat detection in x-ray security images. In the practical setting, the cost of obtaining complete pixel-level annotations increases significantly, which can be reduced by partially labeling the dataset. However, handling partially labeled datasets can lead to training complicated multi-stage networks. In this paper, we propose a new end-to-end object separation framework that trains a single network on a partially labeled dataset while also alleviating the inherent class imbalance at the data and object proposal level. Empirical results demonstrate significant improvement over existing approaches.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8019/_p
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@ARTICLE{e105-d_10_1807,
author={Joanna Kazzandra DUMAGPI, Yong-Jin JEONG, },
journal={IEICE TRANSACTIONS on Information},
title={End-to-End Object Separation for Threat Detection in Large-Scale X-Ray Security Images},
year={2022},
volume={E105-D},
number={10},
pages={1807-1811},
abstract={Fine-grained image analysis, such as pixel-level approaches, improves threat detection in x-ray security images. In the practical setting, the cost of obtaining complete pixel-level annotations increases significantly, which can be reduced by partially labeling the dataset. However, handling partially labeled datasets can lead to training complicated multi-stage networks. In this paper, we propose a new end-to-end object separation framework that trains a single network on a partially labeled dataset while also alleviating the inherent class imbalance at the data and object proposal level. Empirical results demonstrate significant improvement over existing approaches.},
keywords={},
doi={10.1587/transinf.2022EDL8019},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - End-to-End Object Separation for Threat Detection in Large-Scale X-Ray Security Images
T2 - IEICE TRANSACTIONS on Information
SP - 1807
EP - 1811
AU - Joanna Kazzandra DUMAGPI
AU - Yong-Jin JEONG
PY - 2022
DO - 10.1587/transinf.2022EDL8019
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2022
AB - Fine-grained image analysis, such as pixel-level approaches, improves threat detection in x-ray security images. In the practical setting, the cost of obtaining complete pixel-level annotations increases significantly, which can be reduced by partially labeling the dataset. However, handling partially labeled datasets can lead to training complicated multi-stage networks. In this paper, we propose a new end-to-end object separation framework that trains a single network on a partially labeled dataset while also alleviating the inherent class imbalance at the data and object proposal level. Empirical results demonstrate significant improvement over existing approaches.
ER -