[논문]X-Ray Security Checkpoint System Using Storage Media Detection Method Based on Deep Learning for Information Security

Lee, Han-Sung; Kim Kang-San; Kim, Won-Chan; Woo, Tea-Kun; Jung, Se-Hoon

doi:10.9717/kmms.2022.25.10.1433

X-Ray Security Checkpoint System Using Storage Media Detection Method Based on Deep Learning for Information Security 원문보기

멀티미디어학회논문지 = Journal of Korea Multimedia Society, v.25 no.10, 2022년, pp.1433 - 1447

Lee, Han-Sung (School of Creative Convergence, Andong National University) , Kim Kang-San (Co., Ltd TNMTECH) , Kim, Won-Chan (Co., Ltd TNMTECH) , Woo, Tea-Kun (Co., Ltd TNMTECH) , Jung, Se-Hoon (Dept. of Computer Engineering, Sunchon National University)

Abstract ▼ AI-Helper

Recently, as the demand for physical security technology to prevent leakage of technical and business information of companies and public institutions increases, the high tech companies are operating X-ray security checkpoints at building entrances to protect their intellectual property and technology. X-ray security checkpoints are operated to detect cameras and storage media that may store or leak important technologies in the bags of people entering and leaving the building. In this study, we propose an X-ray security checkpoint system that automatically detects a storage medium in an X-ray image using a deep learning based object detection method. The proposed system consists of an edge computing unit and a cloud-computing unit. We employ the RetinaNet for automatic storage media detection in the X-ray security checkpoint images. The proposed approach achieved mAP of 95.92% on private dataset.

주제어

표/그림 (14)

그림 Fig. 1. Overall of architecture of the proposed X-ray security checkpoint system for storage medium detection.
그림 Fig. 2. The operational flow of the edge-computing unit.
그림 Fig. 3. The system configuration diagram of the cloud-computing unit.
표 Table 1. The description of data set for storage medium detection.
그림 Fig. 4. Example images of initial training dataset.
그림 Fig. 5. Example images of test dataset.
표 Table 2. Evaluation Results (Average Precision).
그림 Fig. 6. Example of a storage medium detected by the proposed method.
그림 Fig. 7. Example images of a falsely detected object by the proposed system.
그림 Fig. 8. The structure of class classification using R-CNN.
그림 Fig. 9. The structure of class classification using Fast R-CNN.
그림 Fig. 10. The structure of class classification using Faster R-CNN.
그림 Fig. 11. The structure of class classification using YOLO.
그림 Fig. 12. The structure of class classification using SSD.

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