Automatic detection of prohibited items with small size in X-ray images

Zhang, Yutao; Zhang, Haigang; Zhao, Tengfei; Yang, Jinfeng

doi:10.1007/s11801-020-9118-x

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…Adding various convolutional modules in the network can also improve the performance of small target detection to a certain extent. Zhang et al [8] added empty space on the basis of FSSD.Hole convolution and residual module improve the model's detection effect on small targets. Currently, visual analysis models based on the Transformer framework shine in various visual tasks [9],and its unique self-attention mechanism enables the model to better process global context information.…”

Section: Introductionmentioning

confidence: 99%

DINOray: A Tightly Integrated De-occlusion Model for the Detection of Prohibited Items

Sun,

Wang,

Sun

et al. 2024

Preprint

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In today’s society, the increasing occurrence of terrorist actsand other criminal behaviors highlights the crucial necessity of securityscreening to safeguard areas from potential threats. However, most of theexisting mainstream target detection methods are designed for process-ing natural images, and do not take into account the special differencethat X-ray security images have perspective. The prohibited items withinthese security images exhibit size and shape variations under variousimaging angles, often rendering them uninformative to the models. Inaddition, the security images frequently display varying degrees of over-lapping and occlusion between targets and backgrounds, which makes theaccurate detection of prohibited items more difficult. To address thesechallenges in security screening tasks, this paper proposes a novel modelbased on DINO, which is referred to as DINOray. It integrates a Cross-Scale Tight Integration module and an Occlusion Removal module. Bymitigating noise and occlusion, DINOray extracts more salient informa-tion about prohibited items and tightly integrates it with the output ofthe encoder, which includes global features, in order to supplement lo-cal features. These enhanced features are then passed to the decoder toenhance the detection accuracy of the model. The experimental resultsshow that DINOray outperforms DINO by achieving 3.5% and 1.1% en-hancements in the detection accuracy of prohibited items on the PIDray and CLCXray open-source security screening datasets, respectively. Thisdemonstrates its superior performance in the security screening field.

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Section: Introductionmentioning

confidence: 99%

DINOray: A Tightly Integrated De-occlusion Model for the Detection of Prohibited Items

Sun,

Wang,

Sun

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, they introduced a deformable convolution module to automatically adjust and obtain the contour and scale information of the detection target, enhancing the detection ability for small targets [18]. Zhang Youkang et al proposed an asymmetric convolutional multiview detection network, which uses small convolutional asymmetric networks, multi-scale feature map fusion strategies, and dilated multi-view convolution modules to improve the identification of dangerous goods in X-ray security inspection images under background interference [19]. 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].…”

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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“…Gaus et al [3] employed convolutional neural networks to detect guns in luggage. Because of the adaptability of deep learning algorithms and X-ray images for contraband detection, it has become the main research algorithm of X-ray contraband detection [4,5]. When addressing the problem of X-ray image classification with deep convolutional neural networks, Akçay et al [6] emphasized the importance of a training dataset with strong generalization ability to enhance the neural network's convolution accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on Enhanced Contraband Dataset ACXray Based on ETL

Song,

Yang,

Zhang

et al. 2024

CMC

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To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications, a method has been proposed that employs the Extract-Transform-Load (ETL) approach to create an X-ray dataset of contraband items. Initially, X-ray scatter image data is collected and cleaned. Using Kafka message queues and the Elasticsearch (ES) distributed search engine, the data is transmitted in real-time to cloud servers. Subsequently, contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for quick image retrieval. The method of integrating targets with backgrounds has enhanced the X-ray contraband image data, increasing the number of positive samples. Finally, an Airport Customs X-ray dataset (ACXray) compatible with customs business scenarios has been constructed, featuring an increased number of positive contraband samples. Experimental tests using three datasets to train the Mask Region-based Convolutional Neural Network (Mask R-CNN) algorithm and tested on 400 real customs images revealed that the recognition accuracy of algorithms trained with Security Inspection X-ray (SIXray) and Occluded Prohibited Items X-ray (OPIXray) decreased by 16.3% and 15.1%, respectively, while the ACXray dataset trained algorithm's accuracy was almost unaffected. This indicates that the ACXray dataset-trained algorithm possesses strong generalization capabilities and is more suitable for customs detection scenarios.

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Automatic detection of prohibited items with small size in X-ray images

Cited by 13 publications

References 8 publications

DINOray: A Tightly Integrated De-occlusion Model for the Detection of Prohibited Items

DINOray: A Tightly Integrated De-occlusion Model for the Detection of Prohibited Items

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

Research on Enhanced Contraband Dataset ACXray Based on ETL

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