X-Ray Baggage Inspection With Computer Vision: A Survey
Рентгеновский досмотр багажа с использованием компьютерного зрения: обзор
2020-01-01
SCID: 54.1/w4gf7cth
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X-ray baggage inspectionX-ray multi-viewscomputer visionmulti-energy X-ray testingpublic datasets
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Abstract (AI)
In the last decades, baggage inspection based on X-ray imaging has been established to protect environments in which access control is of vital significance. In several public entrances, like airports, government buildings, stadiums and large event venues, security checks are carried out on all baggage to detect suspicious objects (e.g., handguns and explosives). Although improvements in X-ray technology and computer vision have made many X-ray detection tasks that were previously unfeasible a reality, the progress that has been made in automated baggage inspection is very limited compared to what is needed. For this reason, X-ray screening systems are usually being manipulated by human inspectors. Research and development experts who focus on X-ray testing are moving towards new approaches that can be used to aid human operators. This paper reports the state of the art in baggage inspection identifying three research fields that have been used to deal with this problem:i) X-rayenergies, because there is enough research evidence to show that multi-energy X-ray testing must be used when the material characterization is required;ii) X-raymulti-views, because they can be an effective option for examining complex objects where the uncertainty of only one view can lead to misinterpretation; andiii) X-raycomputer vision algorithms, because there are a plethora of computer vision approaches that can address many 3D object recognition problems. Besides, this paper presents useful public datasets that can be used for training and testing, and also summarizes the reported experimental results in this field. Finally, this paper addresses the general limitations and show new avenues for future research.
Key Findings
1
Automated X-ray baggage inspection remains substantially less advanced than required, so screening systems typically continue to rely on human inspectors.
2
Computer vision algorithms offer approaches for addressing numerous 3D object-recognition problems in X-ray baggage screening.
3
Multi-view X-ray imaging can reduce misinterpretation caused by uncertainty from examining complex objects in a single view.
4
The survey compiles public datasets, summarizes reported experimental results, discusses limitations, and outlines future research directions.
5
The survey identifies multi-energy X-ray imaging as necessary for material characterization in baggage inspection.
Research Object
Automated X-ray baggage inspection systems for detecting suspicious objects in security-screening environments
Research Subject
State-of-the-art approaches and limitations in multi-energy and multi-view X-ray imaging and computer-vision-based recognition for assisting human inspectors in baggage screening
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2020-01-01
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