A real-time Threat Image Projection (TIP) model base on deep learning for X-ray baggage inspection

Wei, Yiru; Zhu, Zhiliang; Yu, Hai; Zhang, Wei

doi:10.1016/j.physleta.2021.127306

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…In TIP, synthetic training images are generated by projecting pre-recorded images of threat items, also called fictional-threat images (FTIs), onto real X-ray images of baggage. Recently, TIP has been used to generate X-ray images of baggage containing threat targets for use in DL model training [7]. However, under a lot of circumstances, TIP images do not look realistic enough due to artifacts or unrealistic scenarios.…”

Section: Introductionmentioning

confidence: 99%

Zero shot domain adaptation for x-ray baggage scanning

Horton

Soneson

et al. 2023

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications

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Deep learning based vision models have had significant success recently. However, one of the biggest challenges is to apply the established models to new targets and environments where no samples exist for training. To solve this zero-shot learning problem, we formulated a heterogeneous learning domain adaptation scenario where labeled data from other domains are available for the new target and for some other unrelated classes. We developed an innovative zero-shot domain adaptation (ZSDA) method by implementing end-to-end adversarial training with class-aware alignment and latent space feature transformation. We demonstrated the performance improvements in several applications in comparison with traditional unsupervised domain adaptation (UDA) approach including threat detection in X-ray security screening imagery.

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Section: Introductionmentioning

confidence: 99%

Zero shot domain adaptation for x-ray baggage scanning

Horton

Soneson

et al. 2023

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications

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“…e research of threat object detection in X-ray images has also made some progress [8][9][10]. In order to generate a large number of X-ray images of bags with threat objects, reat Image Projection (TIP) [11] was developed. A realtime TIP model based on deep learning was proposed.…”

Section: Introductionmentioning

confidence: 99%

X-Ray Image Recognition Based on Improved Mask R-CNN Algorithm

Zhang

Song

Feng

et al. 2021

Mathematical Problems in Engineering

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It is an important part of security inspection to carry out security and safety screening with X-ray scanners. Computer vision plays an important role in detection, recognition, and location analysis in intelligent manufacturing. The object detection algorithm is an important part of the intelligent X-ray machine. Existing threat object detection algorithms in X-ray images have low detection precision and are prone to missed and false detection. In order to increase the precision, a new improved Mask R-CNN algorithm is proposed in this paper. In the feature extraction network, an enhancement path is added to fuse the features of the lower layer into the higher layer, which reduces the loss of feature information. By adding an edge detection module, the training effect of the sample model can be improved without accurate labeling. The distance, overlap rate, and scale difference between objects and region proposals are solved using DIoU to improve the stability of the region proposal’s regression, thus improving the accuracy of object detection; SoftNMS algorithm is used to overcome the problem of missed detection when the objects to be detected overlap each other. The experimental results indicate that the mean Average Precision (mAP) of the improved algorithm is 9.32% higher than that of the Mask R-CNN algorithm, especially for knife and portable batteries, which are small in size, simple in shape, and easy to be mistakenly detected, and the Average Precision (AP) is increased by 13.41% and 15.92%, respectively. The results of the study have important implications for the practical application of threat object detection in X-ray images.

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X-ray Imaging Defect Detection of Transmission Line Strain Clamps Based on a YOLOX Model

Qiu

Shi

et al. 2022

2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)

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A real-time Threat Image Projection (TIP) model base on deep learning for X-ray baggage inspection

Cited by 4 publications

References 5 publications

Zero shot domain adaptation for x-ray baggage scanning

Zero shot domain adaptation for x-ray baggage scanning

X-Ray Image Recognition Based on Improved Mask R-CNN Algorithm

X-ray Imaging Defect Detection of Transmission Line Strain Clamps Based on a YOLOX Model

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