Dangerous goods detection based on transfer learning in X-ray images

Wei, Yuanxi; Liu, Xiaoping

doi:10.1007/s00521-019-04360-0

Cited by 24 publications

(7 citation statements)

References 34 publications

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“…Although the mAP metric was used to assess detection performance in [82], [148], [169], it is not possible to reliably compare the performance due to the variations in the experimental protocols. In [82], adding PDN branch to Faster R-CNN led to improved performance.…”

Section: Gdxray Securitymentioning

confidence: 99%

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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Section: Gdxray Securitymentioning

confidence: 99%

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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“…Zhu et al [ 35 ] achieved image superresolution using dense jump-connected network, where dense unit provides large sensory fields using 3 × 3 convolution, and then, 1 × 1 convolution deepens network to learn robust feature representations, and small filters with a small number of parameters are used to deepen network, making full use of hierarchical feature deepening structure, and network outperforms new algorithm with robust fitting capability. Wei and Liu [ 36 ] introduced dense blocks in SSD network to detect hazardous substances in X-ray images. Dense unit connects two 1 × 1 convolutions in series to avoid destruction of learned feature map region information, and the method has good transfer learning capability.…”

Section: Development Of Densenetmentioning

confidence: 99%

Dense Convolutional Network and Its Application in Medical Image Analysis

Tao

XinYu

et al. 2022

BioMed Research International

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Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.

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“…They were categorized into 5 small, 26 medium, and 7 large objects. Among the small objects, USB flash drives are sensitive storage media linked closely to the leakage of confidential information, whereas bullets, nail clippers, batteries, and lighters are objects that must be detected for flight safety [24][25][26]. Four types of data are provided according to their provision methods, as shown in Table 2.…”

Section: Definition Of Dataset and Object Sizementioning

confidence: 99%

SHOMY: Detection of Small Hazardous Objects using the You Only Look Once Algorithm

2022

KSII TIIS

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Research on the advanced detection of harmful objects in airport cargo for passenger safety against terrorism has increased recently. However, because associated studies are primarily focused on the detection of relatively large objects, research on the detection of small objects is lacking, and the detection performance for small objects has remained considerably low. Here, we verified the limitations of existing research on object detection and developed a new model called the Small Hazardous Object detection enhanced and reconstructed Model based on the You Only Look Once version 5 (YOLOv5) algorithm to overcome these limitations. We also examined the performance of the proposed model through different experiments based on YOLOv5, a recently launched object detection model. The detection performance of our model was found to be enhanced by 0.3 in terms of the mean average precision (mAP) index and 1.1 in terms of mAP (.5:.95) with respect to the YOLOv5 model. The proposed model is especially useful for the detection of small objects of different types in overlapping environments where objects of different sizes are densely packed. The contributions of the study are reconstructed layers for the Small Hazardous Object detection enhanced and reconstructed Model based on YOLOv5 and the non-requirement of data preprocessing for immediate industrial application without any performance degradation.

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Dangerous goods detection based on transfer learning in X-ray images

Cited by 24 publications

References 34 publications

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Dense Convolutional Network and Its Application in Medical Image Analysis

SHOMY: Detection of Small Hazardous Objects using the You Only Look Once Algorithm

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