Baggage Threat Recognition Using Deep Low-Rank Broad Learning Detector

Velayudhan, Divya; Hassan, Taimur; Ahmed, Abdelfatah; Damiani, Ernesto; Werghi, Naoufel

doi:10.1109/melecon53508.2022.9842976

Cited by 10 publications

(1 citation statement)

References 18 publications

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“…Through the analysis of the dual-energy X-ray scanning contraband dataset and operation of related experiments, it is found that they compared with the photographic (optical) object detection dataset, MS-COCO [2] (Microsoft Common Object in Context), and the dataset PASCAL VOC [3]. In the past few years, artificial intelligence technology based on the neural network has been applied to X-ray contraband detection [4][5][6]. However, these algorithms have not yielded satisfactory achievements in contraband detection.…”

Section: Introductionmentioning

confidence: 99%

Material-Aware Path Aggregation Network and Shape Decoupled SIoU for X-ray Contraband Detection

Xiang

Gong

et al. 2023

Electronics

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X-ray contraband detection plays an important role in the field of public safety. To solve the multi-scale and obscuration problem in X-ray contraband detection, we propose a material-aware path aggregation network to detect and classify contraband in X-ray baggage images. Based on YoloX, our network integrates two new modules: multi-scale smoothed atrous convolution (SCA) and material-aware coordinate attention modules (MCA). In SAC, an improved receptive field-enhanced network structure is proposed by combining smoothed atrous convolution, using separate shared convolution, with a parallel branching structure, which allows for the acquisition of multi-scale receptive fields while reducing grid effects. In the MCA, we incorporate a spatial coordinate separation material perception module with a coordinated attention mechanism. A material perception module can extract the material information features in X and Y dimensions, respectively, which alleviates the obscuring problem by focusing on the distinctive material characteristics. Finally, we design the shape-decoupled SIoU loss function (SD-SIoU) for the shape characteristics of the X-ray contraband. The category decoupling module and the long–short side decoupling module are integrated to the shape loss. It can effectively balance the effect of the long–short side. We evaluate our approach on the public X-ray contraband SIXray and OPIXray datasets, and the results show that our approach is competitive with other X-ray baggage inspection approaches.

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