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

Rafiei, Mehdi; Raitoharju, Jenni; Iosifidis, Alexandros

doi:10.1109/access.2023.3234187

Cited by 23 publications

(7 citation statements)

References 166 publications

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“…In the original pyramid model, a parameter σ is introduced as a scale space factor, and new scale layers are obtained by constantly changing the scale factor k. These scale layers form a group, and each group is down-sampled to obtain image representation sequences at multiple scales. The scale space of the image can be defined as the variable scale Gaussian convolution of the original image, as shown in (4).…”

Section: Image Feature Matching Based On Afk Algorithmmentioning

confidence: 99%

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Vision-Driven Dynamic Texture Recognition for Light Bar Intelligent Assembly Process

et al. 2023

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During the production and assembly process of televisions, the efficiency of assembly is limited by manual assembly methods. Conventional image matching algorithms such as SIFT and ORB fail to meet the real-time accuracy requirements of intelligent assembly processes. To address this issue, a novel algorithm called Adaptive Threshold oFAST and rBRIEF-BRISK (AFK) is proposed. This algorithm utilizes the Hamming distance as a similarity measure and integrates AToFAST and rBRIEF-BRISK fusion feature descriptors. Through a comparison of 12 sets of image matching scenarios, it is demonstrated that the AFK algorithm achieves a matching speed 4.5 times faster than the SIFT algorithm, with an image matching accuracy of up to 90%. Additionally, this paper presents the GLMLBP-TOP (Global-Local and Michelson Contrast LBP-Three Orthogonal Planes) dynamic texture recognition method. Through a comparison of 21 sets experiments, it is shown that the comprehensive classification accuracy of the GLMLBP-TOP method is approximately 94.5%, improving the performance of VLBP (87.71%) and LBP-TOP (91.1%) by 6.79% and 3.4% respectively. Real-world application results demonstrate that this method meets the requirements of intelligent assembly processes and has been successfully applied to the LB intelligent assembly process of a certain company's televisions.INDEX TERMS AFK algorithm, feature descriptor fused with AToFAST and rBRIEF-BRISK, GLMLBP-TOP dynamic texture recognition, LB intelligent assembly.

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Section: Image Feature Matching Based On Afk Algorithmmentioning

confidence: 99%

“…With the rapid development of computer image processing technology, computer vision has been widely used in industrial manufacturing processes [1], [2], [3], [4]. The image matching algorithm based on feature points has become one of the most studied matching algorithms at present because of its small amount of computation and fast speed.…”

Section: Introductionmentioning

confidence: 99%

Vision-Driven Dynamic Texture Recognition for Light Bar Intelligent Assembly Process

et al. 2023

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“…The X-ray package security check system is a common security measure in public places, such as airports, subways and railway stations. It can scan the objects in the luggage and detect prohibited items, such as knives, bullets, guns and explosives [1][2][3][4][5]. However, due to the complexity of X-ray images and the phenomenon of object occlusion, manual inspection often struggles to accurately identify potentially dangerous items and suffers from a low stability and accuracy, which poses a considerable risk to public safety.…”

Section: Introductionmentioning

confidence: 99%

SC-YOLOv8: A Security Check Model for the Inspection of Prohibited Items in X-ray Images

Han,

Ma,

Liu

et al. 2023

Electronics

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X-ray package security check systems are widely used in public places, but they face difficulties in accurately detecting prohibited items due to the stacking and diversity of shapes of the objects inside the luggage, posing a threat to personal safety in public places. The existing methods for X-ray image object detection suffer from low accuracy and poor generalization, mainly due to the lack of large-scale and high-quality datasets. To address this gap, a novel large-scale X-ray image dataset for object detection, LSIray, is provided, consisting of high-quality X-ray images of luggage and objects of 21 types and sizes. LSIray covers some common categories that were neglected in previous research. The dataset provides more realistic and rich data resources for X-ray image object detection. To address the problem of poor security inspection, an improved model based on YOLOv8 is proposed, named SC- YOLOv8, consisting of two new modules: CSPnet Deformable Convolution Network Module (C2F_DCN) and Spatial Pyramid Multi-Head Attention Module (SPMA). C2F_DCN uses deformable convolution, which can adaptively adjust the position and shape of the receptive field to accommodate the diversity of targets. SPMA adopts the spatial pyramid head attention mechanism, which can utilize feature information from different scales and perspectives to enhance the representation ability of targets. The proposed method is evaluated through extensive experiments using the LSIray dataset and comparisons with the existing methods. The results show that the method surpasses the state-of-the-art methods on various indicators. Experimenting using the LSIray dataset and the OPIXray dataset, our SC-YOlOv8 model achieves 82.7% and 89.2% detection accuracies, compared to the YOLOv8 model, which is an improvement of 1.4% and 1.2%, respectively. The work not only provides valuable data resources, but also offers a novel and effective solution for the X-ray image security check problem.

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“…Furthermore, recent work has seen the introduction of new paradigms for object detection, such as the use of Vision Transformers [23] and anchorfree models [12,42,43]. However, the performance of all of these object detection approaches is very dependent on the availability of suitable X-ray security imagery datasets with sufficient object annotations, diversity and scale which has often been lacking within the common public X-ray dataset resources [1,25,28].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the creation of dedicated X-ray security datasets has been an important step in the development of APIDS-capable approaches but in itself is inherently challenging due to the requirement for concurrent access to an X-ray security scanner, a diverse range of suitable prohibited threat items and similarly a suitably diverse set of passenger bags in which to em-place them. As a result, a limited number of large-scale benchmark datasets have emerged [26,31,40,41] upon which the relative performance analysis of APIDS capable approaches is now largely reliant [1,5,25,28,39]. Consequently, a statistical review of these benchmark dataset resources and their differences from more conventional object detection benchmark datasets [21], is an important step in improving the effectiveness of object detectors when applied to X-ray security prohibited item detection.…”

Section: Introductionmentioning

confidence: 99%

Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening

Isaac-Medina

Bhowmik

Breckon

2023

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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The rapid progress in automatic prohibited object detection within the context of X-ray security screening, driven forward by advances in deep learning, has resulted in the first internationally-recognized, application-focused object detection performance standard (ECAC Common Testing Methodology for Automated Prohibited Item Detection Systems). However, the ever-increasing volume of detection work in this application area is highly reliant on a limited set of large-scale benchmark detection datasets that are specific to this domain. This study provides a comprehensive quantitative analysis of the underlying distribution of the prohibited item instances in three of the most prevalent X-ray security imagery benchmark and how these correlate against the detection performance of six state-of-the-art object detectors spanning multiple contemporary object detection paradigms. We focus on object size, location and aspect ratio within the image in addition to looking at global properties such as image colour distribution. Our results show a clear correlation between false negative (missed) detections and object size with the distribution of undetected items being statistically smaller in size than those typically found in the corresponding dataset as a whole. For false positive detections, the size distribution of such false alarm instances is shown to differ from the corresponding dataset test distribution in all cases. Furthermore, we observe that onestage, anchor-free object detectors may be more vulnerable to the detection of heavily occluded or cluttered objects than other approaches whilst the detection of smaller prohibited item instances such as bullets remains more challenging than other object types.

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

Cited by 23 publications

References 166 publications

Vision-Driven Dynamic Texture Recognition for Light Bar Intelligent Assembly Process

Vision-Driven Dynamic Texture Recognition for Light Bar Intelligent Assembly Process

SC-YOLOv8: A Security Check Model for the Inspection of Prohibited Items in X-ray Images

Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening

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