Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery

Isaac-Medina, Brian K. S.; Willcocks, Chris G.; Breckon, Toby P.

doi:10.1109/icpr48806.2021.9413007

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…YOLOv3 was employed in [88] as a diffraction pattern detector on X-ray images by the illustrated structure in Fig. 8-d and in [76], [131] to detect dangerous objects in baggage security application. It also was used in [132] to detect defects in casting products.…”

Section: ) Deep Object Detection Architecturesmentioning

confidence: 99%

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

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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Section: ) Deep Object Detection Architecturesmentioning

confidence: 99%

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

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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“…where f 3 3 is the finally denoised low-sampling rate feature, and specifical f 0 3 f 3 . Finally, the material activation feature {f 0 ′, .…”

Section: Bidirectional Enhancementmentioning

confidence: 99%

“…Different from traditional detection tasks, in this scenario, there are various items in the passenger's luggage and random permutations between items, resulting in heavily cluttered X-ray images [1][2][3][4]. Therefore, object detection algorithms for general natural images do not perform well on cluttered X-ray images as in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Atomic number prior guided network for prohibited items detection from heavily cluttered X-ray imagery

et al. 2023

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Prohibited item detection in X-ray images is an effective measure to maintain public safety. Recent prohibited item detection methods based on deep learning has achieved impressive performance. Some methods improve prohibited item detection performance by introducing prior knowledge of prohibited items, such as the edge and size of an object. However, items within baggage are often placed randomly, resulting in cluttered X-ray images, which can seriously affect the correctness and effectiveness of prior knowledge. In particular, we find that different material items in X-ray images have clear distinctions according to their atomic number Z information, which is vital to suppress the interference of irrelevant background information by mining material cues. Inspired by this observation, in this paper, we combined the atomic number Z feature and proposed a novel atomic number Z Prior Guided Network (ZPGNet) to detect prohibited objects from heavily cluttered X-ray images. Specifically, we propose a Material Activation (MA) module that cross-scale flows the atomic number Z information through the network to mine material clues and reduce irrelevant information interference in detecting prohibited items. However, collecting atomic number images requires much labor, increasing costs. Therefore, we propose a method to automatically generate atomic number Z images by exploring the color information of X-ray images, which significantly reduces the manual acquisition cost. Extensive experiments demonstrate that our method can accurately and robustly detect prohibited items from heavily cluttered X-ray images. Furthermore, we extensively evaluate our method on HiXray and OPIXray, and the best result is 2.1% mAP50 higher than the state-of-the-art models on HiXray.

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“…Nassar et al [19] apply a convolutional neural network that takes multi-view images and corresponding geolocation information as inputs and uses a joint loss function considering all views, resulting in an increase of the detection mAP by up to 27.8%. A different approach by Isaac-Medina et al [14] apply a post-processing algorithm to eliminate detections that do not lie in the epipolar line between two views using the probability distribution of objects centroids, improving the overall detection mAP by 2.8%. With a similar application context to one of our evaluation cases, Steitz et al [13] investigate merging features from multi-view X-ray baggage imagery using a 3D pooling layer and rely on geometric constraints that result from multiple 2D detection projections processed through a 3D region proposal network and a 3D region-based alignment layer to achieve improved average precision (+6.73% single class, firearms).…”

Section: A Multi-view Object Detectionmentioning

confidence: 99%

Multi-view Vision Transformers for Object Detection

Isaac-Medina

Willcocks

Breckon

2022

2022 26th International Conference on Pattern Recognition (ICPR)

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Object detection has been thoroughly investigated during the last decade using deep neural networks. However, the inclusion of additional information given by multiple concurrent views of the same scene has not received much attention. In scenarios where objects may appear in obscure poses from certain view points, the use of differing simultaneous views can improve object detection. Therefore, we propose a multi-view fusion network to enrich the backbone features of standard object detection architectures across multiple source and target view points. Our method consists of a transformer decoder for the target view that combines the remaining source views feature maps. In this way, the feature representation of the target view can aggregate feature information from the source view through attention. Our architecture is detector-agnostic, meaning it can be applied across any existing detection backbone. We evaluate performance using YOLOX, Deformable DETR and Swin Transformer baseline detectors, comparing standard single view performance against the addition of our multi-view transformer architecture. Our method achieves a 3% increase of the COCO AP over a four view X-ray security dataset and a slight 0.7% increase on a seven view pedestrian dataset. We demonstrate that the integration of different views using attention-based networks improves the detection performance of multi-view datasets. 1

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Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery

Cited by 11 publications

References 27 publications

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

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

Atomic number prior guided network for prohibited items detection from heavily cluttered X-ray imagery

Multi-view Vision Transformers for Object Detection

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