Research on the application of high-efficiency detectors into the detection of prohibited item in X-ray images

Wei, Yuanxi; Liu, Xiaoping; Liu, Yinan

doi:10.1007/s10489-021-02582-1

Cited by 3 publications

(3 citation statements)

References 40 publications

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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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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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“…Traditional object detection technologies [8] such as key point detection, Histogram of Gradient [9] and Scale-Invariant Feature Transform [10] are not suitable for detecting in complex scenes like shadows [11,12] or on blurred images [13] due to poor generalization and slow execution speed. While object detection algorithms based on Convolutional Neural Network (CNN) have more detection accuracy and are gradually applied in practice [14,15], they have been divided into the two-stage algorithm and the one-stage algorithm. The two-stage algorithms such as Region-CNN (R-CNN) [16], Fast R-CNN [17], Faster R-CNN [18] and Mask R-CNN [19] have better detection accuracy but take longer inference time and lack in real-time detection compared to one-stage algorithms represented by SSD (Single Shot Multibox Detector) [20], YOLO (You Only Look Once) [21] and RetinaNet [22].…”

Section: Introductionmentioning

confidence: 99%

Visual Sorting of Express Packages Based on the Multi-Dimensional Fusion Method under Complex Logistics Sorting

Ren

Liu

et al. 2023

Entropy

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Visual sorting of express packages is faced with many problems such as the various types, complex status, and the changeable detection environment, resulting in low sorting efficiency. In order to improve the sorting efficiency of packages under complex logistics sorting, a multi-dimensional fusion method (MDFM) for visual sorting in actual complex scenes is proposed. In MDFM, the Mask R-CNN is designed and applied to detect and recognize different kinds of express packages in complex scenes. Combined with the boundary information of 2D instance segmentation from Mask R-CNN, the 3D point cloud data of grasping surface is accurately filtered and fitted to determining the optimal grasping position and sorting vector. The images of box, bag, and envelope, which are the most common types of express packages in logistics transportation, are collected and the dataset is made. The experiments with Mask R-CNN and robot sorting were carried out. The results show that Mask R-CNN achieves better results in object detection and instance segmentation on the express packages, and the robot sorting success rate by the MDFM reaches 97.2%, improving 2.9, 7.5, and 8.0 percentage points, respectively, compared to baseline methods. The MDFM is suitable for complex and diverse actual logistics sorting scenes, and improves the efficiency of logistics sorting, which has great application value.

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“…8 Therefore, accurate and rapid detection of nitro compounds is of great significance to human health and environmental protection. 9 So far, several techniques have been developed for the detection of nitro-compounds, 10 and the most commonly used is the X-ray method, 11 which has been widely used in public places such as airports, docks and railway stations. However, the equipment used in this method is somewhat expensive and bulky, which is not suitable for some emergency situations.…”

Section: Introductionmentioning

confidence: 99%

Chitosan-bodipy macromolecular fluorescent probes prepared by click reactions for highly sensitive and selective recognition of 2,4-dinitrophenylhydrazine

2022

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Research on the application of high-efficiency detectors into the detection of prohibited item in X-ray images

Cited by 3 publications

References 40 publications

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

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

Visual Sorting of Express Packages Based on the Multi-Dimensional Fusion Method under Complex Logistics Sorting

Chitosan-bodipy macromolecular fluorescent probes prepared by click reactions for highly sensitive and selective recognition of 2,4-dinitrophenylhydrazine

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