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

Yang, Fenghong; Jiang, Runqing; Yan, Yan; Xue, Jing-Hao; Wang, Biao; Wang, Hanzi

doi:10.1109/tifs.2024.3364368

Cited by 1 publication

(1 citation statement)

References 47 publications

(73 reference statements)

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“…In order to solve the problem of overlapping dangerous goods, Wei et al proposed a de-occlusion attention module (DOAM), which considers the edge information and material information of dangerous goods in X-ray images from the perspective of attention, thereby improving detection performance [20]. In order to solve the problem that some dangerous goods detection methods cannot guarantee good performance when applied to multiple datasets, Yang et al proposed a new dual-mode learning network (DML-Net) to effectively detect dangerous goods in multiple datasets [21].…”

Section: Introductionmentioning

confidence: 99%

Efficient X-ray Security Images for Dangerous Goods Detection Based on Improved YOLOv7

Liu,

Zhang,

et al. 2024

Electronics

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In response to the problems of complex background, multi-scale dangerous goods and severe stacking in X-ray security images, this paper proposes a high-accuracy dangerous goods detection algorithm for X-ray security images based on the improvement of YOLOv7. Firstly, by combining the coordinate attention mechanism, the downsampling structure of the backbone network is improved to enhance the model’s target feature localization ability. Secondly, a weighted bidirectional feature pyramid network is used as the feature fusion structure to achieve multi-scale feature weighted fusion and further simplify the network. Then, combined with dynamic snake convolution, a downsampling structure was designed to facilitate the extraction of features at different scales, providing richer feature representations. Finally, drawing inspiration from the idea of group convolution and combining it with Conv2Former, a feature extraction module called a multi-convolution transformer (MCT) was designed to enhance the network’s feature extraction ability by combining multi-scale information. The improved YOLOv7 in this article was tested on the public datasets SIXRay, CLCXray, and PIDray. The average detection accuracy (mAP) of the improved model was 96.3%, 79.3%, and 84.7%, respectively, which was 4.7%, 2.7%, and 3.1% higher than YOLOv7. This proves the effectiveness and universality of the method proposed in this article. Compared to the current mainstream X-ray image dangerous goods detection models, this model effectively reduces the false detection rate of dangerous goods in X-ray security inspection images and has achieved significant improvement in the detection of small and multi-scale targets, achieving higher accuracy in dangerous goods detection.

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Section: Introductionmentioning

confidence: 99%