Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection

Mery, Domingo; Svec, Erick; Arias, Marco; Riffo, Vladimir; Saavedra, José M.; Banerjee, S.

doi:10.1109/tsmc.2016.2628381

Cited by 144 publications

(68 citation statements)

References 51 publications

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“…With the growth of population in large cities and crowd density in public transportation hubs, it becomes more and more important to fast, automatically and accurately recognize prohibited items in X-ray scanned images. Recent years, the rapid development of deep learning [19] in particular convolutional neural networks has brought an evolution to image processing and visual understanding, including discovering and recognizing objects in X-ray images [23] [27] [24]. Different from natural images and other X-ray scans [35], security inspection often deals with a baggage or suitcase where objects are randomly stacked and heavily overlapped with each other.…”

Section: Introductionmentioning

confidence: 99%

SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images

Miao

Xie

Wan

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

242

246

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In this paper, we present a large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images. Our dataset, named SIXray, consists of 1,059,231 X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated. It raises a brand new challenge of overlapping image data, meanwhile shares the same properties with existing datasets, including complex yet meaningless contexts and class imbalance.

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Section: Introductionmentioning

confidence: 99%

SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images

Miao

Xie

Wan

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

242

246

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“…Convolutional neural networks (CNN), a state-of-the-art paradigm for contemporary computer vision problems, were introduced into the field of X-ray baggage imagery by [7], comparing CNN to a BoVW approach with conventional hand-crafted features trained with a Support Vector Machine (SVM) classifier. Following the work of [7], [8] also studies X-ray baggage object classification with CNN similarly comparing it against traditional classifiers.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery

Akçay

Kundegorski

Willcocks

et al. 2018

IEEE Trans.Inform.Forensic Secur.

312

180

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We consider the use of deep Convolutional Neural Networks (CNN) with transfer learning for the image classification and detection problems posed within the context of X-ray baggage security imagery. The use of the CNN approach requires large amounts of data to facilitate a complex end-to-end feature extraction and classification process. Within the context of Xray security screening, limited availability of object of interest data examples can thus pose a problem. To overcome this issue, we employ a transfer learning paradigm such that a pre-trained CNN, primarily trained for generalized image classification tasks where sufficient training data exists, can be optimized explicitly as a later secondary process towards this application domain. To provide a consistent feature-space comparison between this approach and traditional feature space representations, we also train Support Vector Machine (SVM) classifier on CNN features. We empirically show that fine-tuned CNN features yield superior performance to conventional hand-crafted features on object classification tasks within this context. Overall we achieve 0.994 accuracy based on AlexNet features trained with Support Vector Machine (SVM) classifier. In addition to classification, we also explore the applicability of multiple CNN driven detection paradigms such as sliding window based CNN (SW-CNN), Faster RCNN (F-RCNN), Region-based Fully Convolutional Networks (R-FCN) and YOLOv2. We train numerous networks tackling both single and multiple detections over SW-CNN/F-RCNN/R-FCN/YOLOv2 variants. YOLOv2, Faster-RCNN, and R-FCN provide superior results to the more traditional SW-CNN approaches. With the use of YOLOv2, using input images of size 544×544, we achieve 0.885 mean average precision (mAP) for a six-class object detection problem. The same approach with an input of size 416×416 yields 0.974 mAP for the two-class firearm detection problem and requires approximately 100ms per image. Overall we illustrate the comparative performance of these techniques and show that object localization strategies cope well with cluttered X-ray security imagery where classification techniques fail.

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“…Therefore, feature points extraction will replace the edge detection to detect location of surgical stitching in our approach. Regarding feature points detection, Harris corner detection [15], scale-invariant feature transform (SIFT) [16], and speeded-up robust features (SURF) [17] are methods to be evaluated. The SIFT is probably the most well-known feature points extraction method.…”

Section: Image Feature Extractionmentioning

confidence: 99%

Surgical Wounds Assessment System for Self-Care

Chen

Hsu

Hung

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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The importance of effective surgical wound care cannot never be underestimated. Poorly managing surgical wounds may cause many serious complications. Thus, it raises the necessity to develop a patient-friendly self-care system which can help both patients and medical professionals to ensure the state of the surgical wounds without any special medical equipment. In this paper, a surgical wound assessment system for self-care is proposed. The proposed system is designed to enable patients capture surgical wound images of themselves by using a mobile device and upload these images for analysis. Combining image-processing and machine-learning techniques, the proposed method is composed of four phases. First, images are segmented into superpixels where each superpixel contains the pixels in the similar color distribution. Second, these superpixels corresponding to the skin are identified and the area of connected skin superpixels is derived. Third, surgical wounds will be extracted from this area based on the observation of the texture difference between skin and wounds. Lastly, state and symptoms of surgical wound will be assessed. Extensive experimental results are conducted. With the proposed method, more than 90% state assessment results are correct and more than 91% symptom assessment results consistent with the actual diagnosis. Moreover, case studies are provided to show the advantage and limitation of this system. These results show that this system could perform well in the practical self-care scenario.Index Terms-Artificial intelligence (AI), classification, health care service systems.

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Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection

Cited by 144 publications

References 51 publications

SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images

SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images

Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery

Surgical Wounds Assessment System for Self-Care

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