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MFA-net: Object detection for complex X-ray cargo and baggage security imagery
- Title
- MFA-net: Object detection for complex X-ray cargo and baggage security imagery
- Authors
- Viriyasaranon, Thanaporn; Chae, Seung-Hoon; Choi, Jang-Hwan
- Ewha Authors
- 최장환
- SCOPUS Author ID
- 최장환
- Issue Date
- 2022
- Journal Title
- PLOS ONE
- ISSN
- 1932-6203
- Citation
- PLOS ONE vol. 17, no. 9
- Publisher
- PUBLIC LIBRARY SCIENCE
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Deep convolutional networks have been developed to detect prohibited items for automated inspection of X-ray screening systems in the transport security system. To our knowledge, the existing frameworks were developed to recognize threats using only baggage security X-ray scans. Therefore, the detection accuracy in other domains of security X-ray scans, such as cargo X-ray scans, cannot be ensured. We propose an object detection method for efficiently detecting contraband items in both cargo and baggage for X-ray security scans. The proposed network, MFA-net, consists of three plug-and-play modules, including the multiscale dilated convolutional module, fusion feature pyramid network, and auxiliary point detection head. First, the multiscale dilated convolutional module converts the standard convolution of the detector backbone to a conditional convolution by aggregating the features from multiple dilated convolutions using dynamic feature selection to overcome the object-scale variant issue. Second, the fusion feature pyramid network combines the proposed attention and fusion modules to enhance multiscale object recognition and alleviate the object and occlusion problem. Third, the auxiliary point detection head adopts an auxiliary head to predict the new keypoints of the bounding box to emphasize the localizability without requiring further ground-truth information. We tested the performance of the MFA-net on two large-scale X-ray security image datasets from different domains: a Security Inspection X-ray (SIXray) dataset in the baggage domain and our dataset, named CargoX, in the cargo domain. Moreover, MFA-net outperformed state-of-the-art object detectors in both domains. Thus, adopting the proposed modules can further increase the detection capability of the current object detectors on X-ray security images.
- DOI
- 10.1371/journal.pone.0272961
- Appears in Collections:
- 인공지능대학 > 인공지능학과 > Journal papers
- Files in This Item:
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journal.pone.0272961.pdf(2.5 MB)
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