Material-aware Cross-channel Interaction Attention (MCIA) for occluded prohibited item detection

Du, Huiqian

doi:10.1007/s00371-022-02498-y

Cited by 10 publications

(6 citation statements)

References 37 publications

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“…PAA [34] and YOLOv6 [29]. The prohibited object detection methods in X-ray images include MFFNet [40]，FAB [39]，MCIA [13]，DOAM [57]，LA [42]，FA [58]，EAOD-Net [12], Ma et al [59] and POD [38]。 For SIXray dataset, the IoU threshold is set as 0.5 to calculate the mAP. For CLCXray, COCO evaluation criteria is adopted.…”

Section: Comparison With the State-of-the-arts Methodsmentioning

confidence: 99%

“…In order to solve the occlusion problem in X-ray images, Wang et al [13] proposed a Material-aware Cross-channel Interaction Attention mechanism (MCIA), which includes two key modules, i.e Material Perception module and Cross-channel Interaction module. The former is to enhance the object material information and the latter to perform local cross-channel interaction.…”

Section: X-ray Prohibited Object Detectionmentioning

confidence: 99%

“…Recently, some attention mechanisms have been proposed for the prohibited object detection tasks in X-ray images and improved the detection performance significantly [12][13][14], including SA(Spatial Attention), MCIA(Material-aware Cross-channel Interaction Attention) and GAM(Global Attention Mechanism). However，these mechanisms are channel-wise or pixel-wise, which cannot suppress the features of background regions well.…”

Section: Introductionmentioning

confidence: 99%

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CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

Zhang

et al. 2023

Preprint

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Fast and accurate prohibited object detection in X-ray images is great challenging. Based on YOLOv6 object detection framework, in this paper, Channel-Target Attention Feature Pyramid Network (CTA-FPN) is proposed for prohibited object detection in X-ray images. It includes two key components: TAAM (Target Aware Attention Module) and CAM (Channel Attention Module). TAAM is to generate the target attention map to enhance the features of prohibited object regions and suppress those of the background regions, so as to solve the problems of object occlusion and cluttered background in X-ray images. CAM is to highlight the feature channels important to the detection tasks, and suppress the irrelevant ones. The target-wise and channel-wise feature enhancement can effectively strengthen the feature representation capability of the network. The proposed CTA-FPN is incorporated into S, M and L models of YOLOv6 respectively, obtaining three X-ray prohibited object detection models. The experimental results on two publicly available benchmark datasets of SIXray and CLCXray show that, CTA-FPN can effectively improve the detection performance of YOLOv6. Especially, YOLOv6-CTA-FPN-L can achieve the state-of-the-arts detection accuracy.

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Section: Comparison With the State-of-the-arts Methodsmentioning

confidence: 99%

Section: X-ray Prohibited Object Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

Zhang

et al. 2023

Preprint

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“…The method involved included object detection models such as Swin Transformer [44], RetinaNet [45], DetectoRS [46], Yolov5 and baseline YoloX. It also includes the most advanced contraband detection models in the last two years such as CHR [10], FBS [47], CFPA-Net [48], MCIA-FPN [49] and POD-Y [21]. As Tables 1 and 2 show, the proposed model can achieve the optimal detection performance on the OPIXray and SIXray datasets; the mAP values are 2.02% and 0.71% higher than those of the state-of-the-art model on the OPIXray and SIXray datasets.…”

Section: Comparing With Sota Detection Methodsmentioning

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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“…With the rapid development of deep learning, a large number of X-ray security image detection methods [4], [5], [14]- [18] have been proposed to learn effective feature representations of prohibited items and improve the detection accuracy. Jaccard et al [14] first develop a deep learning scheme for the detection of small metallic threats in X-ray cargo images and validate the superior performance of Convolutional Neural Networks (CNNs) over traditional methods.…”

Section: A X-ray Security Image Detectionmentioning

confidence: 99%

Dual-Mode Learning for Multi-Dataset X-Ray Security Image Detection

Yang,

Jiang,

Yan

et al. 2024

IEEE Trans.Inform.Forensic Secur.

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Material-aware Cross-channel Interaction Attention (MCIA) for occluded prohibited item detection

Cited by 10 publications

References 37 publications

CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

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

Dual-Mode Learning for Multi-Dataset X-Ray Security Image Detection

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