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

Miao, Caijing; Xie, Lingxi; Wan, Fang; Su, Chi; Liu, Hongye; Jiao, J. B.; Ye, Qixiang

doi:10.1109/cvpr.2019.00222

Cited by 242 publications

(296 citation statements)

References 35 publications

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“…In recent years, 2D X-ray image based threat object recognition has been extensively studied [1,2,24], although even the use of multiple view X-ray suffers from the challenges of object recognition under varying orientation and inter-object occlusion. This can be addressed by 3D X-ray CT imaging which provides abundant information as a 3D volume comprised of successive, parallel X-ray image slices [14].…”

Section: Related Workmentioning

confidence: 99%

An approach for adaptive automatic threat recognition within 3D computed tomography images for baggage security screening

Wang

Ismail

Breckon

2020

XST

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BACKGROUND: The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material signatures in contrast to adaptability towards new and emerging threat signatures. To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work.OBJECTIVE: In this paper, we present a solution to AATR based on such X-ray CT baggage scan imagery. This aims to address the issues of rapidly evolving threat signatures within the screening requirements. Ideally, the detection algorithms deployed within the security scanners should be readily adaptable to different situations with varying requirements of threat characteristics (e.g., threat material, physical properties of objects).METHODS: We tackle this issue using a novel adaptive machine learning methodology with our solution consisting of a multi-scale 3D CT image segmentation algorithm, a multiclass support vector machine (SVM) classifier for object material recognition and a strategy to enable the adaptability of our approach. Experiments are conducted on both open and sequestered 3D CT baggage image datasets specifically collected for the AATR study.RESULTS: Our proposed approach performs well on both recognition and adaptation. Overall our approach can achieve the probability of detection around 90% with a probability of false alarm below 20%. Figure 1: Threat objects in 2D X-ray images [2]. Left: threat object signatures of specific appearance (e.g., firearms) are easy to identify. Right: threat materials are more challenging to recognize in cluttered X-ray images.

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Section: Related Workmentioning

confidence: 99%

An approach for adaptive automatic threat recognition within 3D computed tomography images for baggage security screening

Wang

Ismail

Breckon

2020

XST

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“…Methods such as Faster R-CNN [11], R-FCN [18], and YOLOv2 [19], achieves maximal 0.885 and 0.974 mean Average Precision (mAP) over six-class object detection {Firearm, Firearm Component, Ceramic Knife, Laptop, Camera, Knife} and and two-class firearm detection {Firearm, Firearm Component} problems respectively. Here, we further investigate this performance with our evaluation on X-ray security imagery on larger dataset taken from different source, namely Durham University Full Two-Class and Security Inspection X-ray images [10]. Denoted as Dbf2 [9] and SIXray [10] respectively, it provide more inter-occluding Xray security imagery examples with large variations in pose, scale and item construction.…”

Section: Related Workmentioning

confidence: 99%

“…Here, we further investigate this performance with our evaluation on X-ray security imagery on larger dataset taken from different source, namely Durham University Full Two-Class and Security Inspection X-ray images [10]. Denoted as Dbf2 [9] and SIXray [10] respectively, it provide more inter-occluding Xray security imagery examples with large variations in pose, scale and item construction.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 2A depicts exemplary images from this dataset. SIXray is public dataset [10] for prohibited item detection in security inspection X-ray images. These images are collected from several metro sub-way stations, using a Nuctech dualenergy X-ray scanner, where the distribution of the general baggage/parcel items corresponds to stream-of-commerce occurrence.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…localize) prohibited items within X-ray baggage security imagery whilst determining the varying performance of these approaches on different datasets collected for this study. In this approach, we fully utilize large-scale X-ray security imagery provided by [9] and [10]. Denoted as Dbf2 [9] and SIXray [10] respectively, we aim to provide an insight into baseline performance for three CNN architectural variants:-Faster R-CNN [11], Mask R-CNN [12] and RetinaNet [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery

Gaus

Bhowmik

Breckon

2019

2019 IEEE International Symposium on Technologies for Homeland Security (HST)

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Research on Object Detection in Complex Scenarios Based on ASA‐YOLOv5

Lin,

Deng,

Zhang

et al. 2024

IEEJ Transactions Elec Engng

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The applications of target detection in complex scenarios cover a wide range of fields, such as pedestrian and vehicle detection in self‐driving cars, face recognition and abnormal behavior detection in security monitoring systems, hazardous materials safety detection in public transportation, and so on. These applications demonstrate the importance and the prospect of wide application of target detection techniques in solving practical problems in complex scenarios. However, in these real scenes, there are often problems such as mutual occlusion and scale change. Therefore, how to accurately identify the target in the real complex scenarios has become a big problem to be solved. In order to solve the above problem, the paper proposes a novel algorithm, Adaptive Self‐Attention‐YOLOv5 (ASA‐YOLOv5), which is built upon the YOLOv5s algorithm and demonstrates effectiveness for target identification in complex scenarios. First, the paper implements a fusion mechanism between the trunk and neck networks, enabling the fusion of features across different levels through upsampling and downsampling. This fusion process mitigates detection errors caused by feature loss. Second, the Shuffle Attention mechanism is introduced before upsampling and downsampling to suppress noise and amplify essential semantic information, further enhancing target identification accuracy. Lastly, the Adaptively Spatial Feature Fusion (ASFF) module and Receptive Field Blocks (RFBs) module are added in the head network, and it can improve feature scale invariance and expand the receptive field. The ability of the model to detect the target in the complex scene is improved effectively. Experimental results indicate a notable improvement in the model's mean Average Precision (mAP) by 2.1% on the COCO dataset and 0.7% on the SIXray dataset. The proposed ASA‐YOLOv5 algorithm can enhance the effectiveness for target detection in complex scenarios, and it can be widely used in real‐world settings. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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

Cited by 242 publications

References 35 publications

An approach for adaptive automatic threat recognition within 3D computed tomography images for baggage security screening

An approach for adaptive automatic threat recognition within 3D computed tomography images for baggage security screening

On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery

Research on Object Detection in Complex Scenarios Based on ASA‐YOLOv5

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