Transferring x-ray based automated threat detection between scanners with different energies and resolution

Caldwell, Matthew; Ransley, Mark; Rogers, Thomas W.; Griffin, Lewis D.

doi:10.1117/12.2277641

Cited by 5 publications

(2 citation statements)

References 13 publications

(12 reference statements)

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“…Caldwell et al [83] investigates the generalization ability of models trained with different datasets from various scanners. The authors created training and test samples from single or multiple domains to investigate the effect of migration between other models.…”

Section: A Classificationmentioning

confidence: 99%

Object Detection and X-Ray Security Imaging: A Survey

Yang

2023

IEEE Access

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Security is paramount in public places such as subways, airports, and train stations, where security screeners use X-ray imaging technology to check passengers' luggage for potential threats. To streamline this process and make it more efficient, researchers have turned to object detection techniques with the help of deep learning. While some progress has been made, there are few comprehensive literature reviews. This paper provides a comprehensive overview of the standard object detection algorithms and principles in X-ray dangerous goods detection. The article begins by classifying and describing the more popular deep learning object detection techniques in detail and presenting the commonly used publicly available datasets and metrics. And then go on to summarize previous applications of deep learning techniques in X-ray dangerous goods detection, highlighting their successes and limitations. Finally, based on an analysis of the experimental results, it summarizes some of the limitations of deep learning in X-ray baggage detection thus far. It offers insights into the future of this exciting field. With this review, we hope to provide valuable insights and guidance for those seeking to improve public safety through X-ray imaging and deep learning technology.INDEX TERMS Object detection, deep learning, x-ray image detection, baggage security, yolo.

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Section: A Classificationmentioning

confidence: 99%

Object Detection and X-Ray Security Imaging: A Survey

Yang

2023

IEEE Access

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“…In the field of anomaly object detection for X‐ray images, there are many datasets released, such as DBF6 [20], MV‐Xray [25], UBA [41], FFOB [42], UCL TIP [43], SASC [44], Smiths‐Duke [12], GDXray [45], SIXray [46], OPIXray [47], and so on. However, only GDXray, SIXray, and OPIXray are public, and the rest are private.…”

Section: Performance Analysismentioning

confidence: 99%

EAOD‐Net: Effective anomaly object detection networks for X‐ray images

et al. 2022

IET Image Processing

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Anomaly object detection is the core technology in the application for X‐ray images. However, the accuracy of current X‐ray anomaly object detection method still needs to be improved. In this paper, an effective anomaly object detection network is proposed to improve the detection accuracy of anomaly object for X‐ray images. Firstly, learnable Gabor convolution layer, deformable convolution, and spatial attention mechanism are introduced to enhance the representative ability of features in ResNeXt. Then, dense local regression is applied to predict the offset of multiple dense boxes in region proposal to locate the object accurately. At last, bigger discriminative RoI pooling is proposed to classify the candidate boxes to improve the accuracy of object classification. Experimental results on the SIXray and OPIXray datasets show that compared with the state‐of‐the‐art methods, the proposed EAOD‐Net can achieve the competitive detection performance.

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Balanced Affinity Loss for Highly Imbalanced Baggage Threat Contour-Driven Instance Segmentation

Ahmed

Obeid

Velayudhan

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Autonomous detection of threat items from baggage X-ray imagery is one of the most vital and challenging tasks. Manual detection of these items is a cumbersome, slow, and error-ridden process which is also limited by the examination capacity of the security inspector. To overcome these limitations, many researchers have proposed deep learning-driven approaches to recognize suspicious objects from the baggage X-ray scans. However, threat items are rarely seen in the real world compared to innocuous baggage content. Therefore, when trained with imbalanced data, the performance of the conventional threat detection models drastically decreases. This paper addresses these issues with a contour-driven instance segmentation model optimized with a novel combined loss function, dubbed balanced affinity loss function. In addition to mitigating the class imbalance, this function best handles the fine-grained classification aspect inferred by contours and the instance segmentation. We validated the proposed system on three public baggage X-ray datasets, where it outperformed state-of-the-art methods by 7.76%, 25.81%, and 8.78% in terms of intersection-over-union score.

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Transferring x-ray based automated threat detection between scanners with different energies and resolution

Cited by 5 publications

References 13 publications

Object Detection and X-Ray Security Imaging: A Survey

Object Detection and X-Ray Security Imaging: A Survey

EAOD‐Net: Effective anomaly object detection networks for X‐ray images

Balanced Affinity Loss for Highly Imbalanced Baggage Threat Contour-Driven Instance Segmentation

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