Optimization and Research of Suspicious Object Detection Algorithm in X-ray Image

Wang, Ruxue; Shi, Yuliang; Cai, Mingyu

doi:10.1109/itnec56291.2023.10082660

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…As the demand for people's travel increases, the safety of public transportation hubs such as airports, train stations and subway stations is of great concern.X-ray baggage screening is one of the necessary measures to ensure people's travel security, which can detect and intercept people or items carrying contraband in a timely manner 1 . However, the traditional security screening method mainly relies on manual identification of contraband in X-ray images, which has low efficiency, time consuming, high false positive rate, and an accuracy rate of only 80-90% 2,3 . Therefore, how to enhance the efficiency and precision of security checks has become a pressing issue in the field of security screening.…”

Section: Introductionmentioning

confidence: 99%

Design of security image contraband detection system based on PP-YOLOE+_DCS

Chen,

Jin,

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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Traditional security screening methods mainly use manual identification of security images, there is a low identification of inefficiency, high error of judgement rate,which has become a bottleneck limiting public safety and security. Therefore, to deal with this problem, this paper proposes the security image contraband detection model PP-YOLOE+_DCS, which makes three main improvements on the basis of the PP-YOLOE+ model: To begin with, we introduced deformable convolution within the backbone network that strengthen the models' feature extraction capability; Secondly, we introduced a coordinated attention mechanism among the backbone network and the detection neck for better focusing the model on the object region;Finally, we replaced the original GIOU loss function with the SIOU loss function to improve the detection accuracy and training speed. The improved PP_YOLOE+_DCS model obtained achieves 91.4% detection accuracy,2.8% improvement compared with the baseline model mAP, only 0.24M additional parameters,and 420.2ms inference delay on embedded devices, which provides a new solution for the intelligence of contraband detection.

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Section: Introductionmentioning

confidence: 99%

Design of security image contraband detection system based on PP-YOLOE+_DCS

Chen,

Jin,

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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“…Ma et al[31] used the learnable Gabor convolutional layer to capture the edge information of foreground objects and proposed the Dual Scale Feature Aggregation module, which can combine the global features extracted by the Global Context Feature Extraction module with features from another layer. Wang et al[32] SIMA ET AL.…”

mentioning

confidence: 99%

Multi‐Scale Feature Attention‐DEtection TRansformer: Multi‐Scale Feature Attention for security check object detection

Sima,

Chen,

Tang

et al. 2024

IET Computer Vision

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X‐ray security checks aim to detect contraband in luggage; however, the detection accuracy is hindered by the overlapping and significant size differences of objects in X‐ray images. To address these challenges, the authors introduce a novel network model named Multi‐Scale Feature Attention (MSFA)‐DEtection TRansformer (DETR). Firstly, the pyramid feature extraction structure is embedded into the self‐attention module, referred to as the MSFA. Leveraging the MSFA module, MSFA‐DETR extracts multi‐scale feature information and amalgamates them into high‐level semantic features. Subsequently, these features are synergised through attention mechanisms to capture correlations between global information and multi‐scale features. MSFA significantly bolsters the model's robustness across different sizes, thereby enhancing detection accuracy. Simultaneously, A new initialisation method for object queries is proposed. The authors’ foreground sequence extraction (FSE) module extracts key feature sequences from feature maps, serving as prior knowledge for object queries. FSE expedites the convergence of the DETR model and elevates detection accuracy. Extensive experimentation validates that this proposed model surpasses state‐of‐the‐art methods on the CLCXray and PIDray datasets.

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Optimization and Research of Suspicious Object Detection Algorithm in X-ray Image

Cited by 2 publications

References 17 publications

Design of security image contraband detection system based on PP-YOLOE+_DCS

Design of security image contraband detection system based on PP-YOLOE+_DCS

Multi‐Scale Feature Attention‐DEtection TRansformer: Multi‐Scale Feature Attention for security check object detection

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