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학술지 MFA-net: Object detection for complex X-ray cargo and baggage security imagery
Cited 5 time in scopus Download 180 time Share share facebook twitter linkedin kakaostory
Thanaporn Viriyasaranon, 채승훈, 최장환
PLOS ONE, v.17 no.9, pp.1-20
Public Library of Science
22FR2100, 컨테이너 위험화물 자동검색 및 복합탐지 시스템 개발, 정지욱
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.
KSP 제안 키워드
Automated Inspection, Bounding Box, Deep Convolutional Networks, Detection Method, Detection accuracy, Different domains, Dynamic feature selection, Feature selection(FS), Fusion feature, Image datasets, Object Recognition
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