An automated detection model of threat objects for X-ray baggage inspection based on depthwise separable convolution

Wei, Yiru; Zhu, Zhiliang; Yu, Hai; Zhang, Wei

doi:10.1007/s11554-020-01051-1

Cited by 15 publications

(4 citation statements)

References 25 publications

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“…The author Wei Zhang et al, introduced a detection model through an automated mode for threat items based on depth wise and combination of three pretrained models. One of the best parts of this model is high detection accuracy, fast computational speed [3]. Priscilla Steno et al, made research which aims to the improvement of faster R-CNN.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis on airport security of baggage screening using deep learning

Suraj,

Malyada,

Anjali

et al. 2023

ACE

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Airports are the places where landings and take offs of the flights take place. In general, airports are preferred for traveling over large distances. While traveling in flights we carry our baggage which consists of various items, but the items that are prohibited in the baggage are scissors, knives, gun, and the electronic devices which cannot be switched off. Carrying dangerous items may lead to hijacking of flights and it even led to unusual situations in the airport. To prevent public from threat, airports generally have security inspection in which you will be required to pass your luggage through an X-ray machine where it will be checked for dangerous items. This analysis deals with software side of the security inspection and it is a brief study on various state-of-art proposed deep learning intended methods that are used to detect threat in baggage screening, advantages of their methodology, limitations, and gaps to be implemented.

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Section: Literature Surveymentioning

confidence: 99%

Analysis on airport security of baggage screening using deep learning

Suraj,

Malyada,

Anjali

et al. 2023

ACE

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“…In recent years, object detection based on deep learning has made great advances, and various object detection methods have emerged [12][13][14][15]. The researchers proposed some anomaly object detection methods for X-ray images [16][17][18], which refer to the framework of object detection in natural images. Akcay et al [19] has pioneered the research work on using deep learning to classify the anomaly objects for X-ray images.…”

Section: Related Workmentioning

confidence: 99%

EAOD‐Net: Effective anomaly object detection networks for X‐ray images

et al. 2022

IET Image Processing

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Anomaly object detection is the core technology in the application for X‐ray images. However, the accuracy of current X‐ray anomaly object detection method still needs to be improved. In this paper, an effective anomaly object detection network is proposed to improve the detection accuracy of anomaly object for X‐ray images. Firstly, learnable Gabor convolution layer, deformable convolution, and spatial attention mechanism are introduced to enhance the representative ability of features in ResNeXt. Then, dense local regression is applied to predict the offset of multiple dense boxes in region proposal to locate the object accurately. At last, bigger discriminative RoI pooling is proposed to classify the candidate boxes to improve the accuracy of object classification. Experimental results on the SIXray and OPIXray datasets show that compared with the state‐of‐the‐art methods, the proposed EAOD‐Net can achieve the competitive detection performance.

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“…Recently, under the development of computer software and hardware and deep learning technology, detection technology based on computer vision has been widely used in industry, agriculture, security and other fields and achieved good results. Some scholars have also applied it to prohibited item detection, among which Turcsany et al [1] applied the classical BoVW model, which had a series of feature point detectors and descriptors, and was supported by support vector machine and random forest, and achieved more accurate detection results on large X-ray baggage image datasets; According to the shape information of contraband, Literature [2] proposed to use implicit shape model for threat detection, which was based on visual vocabulary and a generation structure. The detection effect was better on the dataset including blades, darts and pistols, but the dataset used was simple and cannot verify the recognition effect in real scenes; Multi-view detection [3,4] can provide more abundant information than single view detection.…”

Section: Introductionmentioning

confidence: 99%

X-ray Image Prohibited Item Detection Algorithm Based on Improved PP-YOLO

Ji-Kai Zhang,

Yue Liu

et al. 2023

Journal of Computers

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<p>In order to solve the problems of missing detection due to overlap and occlusion of contraband in X-ray images and low accuracy of small object detection, we propose a single-stage object detection framework based on PP-YOLO. Compared with the traditional prohibited item detection algorithm, it adds CBAM module on the basis of ResNet50 feature extraction network to enhance the feature extraction ability; For increasing the detail features of the detection layer, MSF module is introduced into FPN, which fuses the feature map with accurate position information in the lower layer and the feature map with strong semantic information in the higher layer; The partial convolution of backbone is improved to CompConv to accelerate the processing speed of the model, which compresses the network structure and improves the inference speed without losing performance. The results show that the mAP of the improved network for prohibited item detection is 94.67%, and the processing speed reaches 45 FPS, which means that the recognition accuracy and reasoning speed of this method have been improved to some extent.</p> <p> </p>

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An automated detection model of threat objects for X-ray baggage inspection based on depthwise separable convolution

Cited by 15 publications

References 25 publications

Analysis on airport security of baggage screening using deep learning

Analysis on airport security of baggage screening using deep learning

EAOD‐Net: Effective anomaly object detection networks for X‐ray images

X-ray Image Prohibited Item Detection Algorithm Based on Improved PP-YOLO

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