Toward Human-in-the-Loop Prohibited Item Detection in X-ray Baggage Images

Cao, Sisi; Liu, Yuehu; Song, Wenwen; Cui, Zhichao; Lv, Xiaojun; Wan, Jian

doi:10.1109/cac48633.2019.8996933

Cited by 4 publications

(3 citation statements)

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“…Another simple foregroundbackground segmentation technique based on color thresholds was applied in [64] as a preprocessing step for object detection in the X-ray images. To increase the trust in automatic detectors on baggage security imagery applications, a human-in-the-loop detection framework was presented in [82]. The framework gives a score to each prohibited item proposal and, based on the score, the baggage is assigned to safe, suspicious, or dangerous classes.…”

Section: Securitymentioning

confidence: 99%

“…Faster R-CNN was applied in [98] and [64], [78], [83], [127] to detect defects in tires and prohibited items in baggage, respectively. In [82], an additional branch called Part-based Detection Network (PDN) was added to Faster R-CNN (Fig. 8-b) to improve detection of occluded items in threat detection on X-ray security images.…”

Section: ) Deep Object Detection Architecturesmentioning

confidence: 99%

“…Although the mAP metric was used to assess detection performance in [82], [148], [169], it is not possible to reliably compare the performance due to the variations in the experimental protocols. In [82], adding PDN branch to Faster R-CNN led to improved performance. In [148], a learningbased image synthesis method was proposed to generate more training data.…”

Section: Gdxray Securitymentioning

confidence: 99%

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Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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Section: Securitymentioning

confidence: 99%