CH-Net: Deep adversarial autoencoders for semantic segmentation in X-ray images of cabin baggage screening at airports

Chouai, Mohamed; Merah, Mostefa; Mimi, Malika

doi:10.1007/s12198-020-00211-5

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…Deep learning models have seen significant success in a wide range of applications in recent years. In fact, different deep learning methods have been introduced for image and motion segmentation [10][11][12][13], detection [14][15][16], tracking [17,18] and classification [19,20]. Due to the success of deep learning methods, CNN models have also been used in the literature to provide relevant information on the number of people present on the stage.…”

Section: Related Workmentioning

confidence: 99%

New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

Chouai

Doležel

Štursa

et al. 2021

Sensors

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In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.

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Section: Related Workmentioning

confidence: 99%

New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

Chouai

Doležel

Štursa

et al. 2021

Sensors

Self Cite

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“…Consequently, exactly to say, Encoder-Decoder is not a specific model, but a kind of framework. The Encoder and Decoder module can be any text, voice, image, video data, and the model can be CNN [38] [39], RNN [40] [41], BiRNN [42] [43], LSTM [44], GRU [40], etc. Therefore, we can design a variety of application algorithms based on the Encoder-Decoder framework.…”

Section: Deep Semantic Segmentation Networkmentioning

confidence: 99%

IIE-SegNet: Deep Semantic Segmentation Network With Enhanced Boundary Based on Image Information Entropy

Wang

et al. 2021

IEEE Access

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With the vigorous development of deep learning and the widespread use of mobile robots, automatic driving has gradually become a research hotspot. Environment perception is the most important part of automatic driving technology, and the purpose of environment perception is to distinguish the environmental content. Therefore, accurate and efficient image semantic segmentation method is becoming more and more important. In this paper, we introduce a deep semantic segmentation solution: IIE-SegNet: Deep semantic segmentation network with enhanced boundary based on image information entropy. At present, deep learning based on semantic segmentation solutions has some problems, such as low segmentation accuracy for small-scale objects and unclear boundary of segmented objects. Our method preserves the boundary of the segmentation object, and has higher segmentation accuracy for small-scale objects. In our method, the features of the underlying pooling layer are added to the ASPP structure of the encoding module, and the image information entropy of the previous pooling layers is introduced into the decoding module. We also introduce focal loss to solve the problem of imbalance between positive and negative samples. Finally, the test results on the extended Pascal VOC 2012 test set, abbreviated to Exp-Pascal VOC 2012 test set show that the proposed method has better performance on Exp-Pascal VOC 2012 test set compared with the advanced methods at the present stage, the segmentation accuracy of small-scale targets is higher, and the boundary is clearer.

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“…The need of having a non-destructive procedure for examining the interior of objects to assess their structural patterns or constituent contents has resulted in many applications of X-ray technology in different fields. While the medical field was one of the first to use the technology for assessing the inner parts of the body [1], the use of X-ray technology is expanding considerably for industrial and security purposes [2], [3]. Factories can now assess whether there are anomalies or defects inside a product without destroying it [4], and border patrol officers at security gates can check for forbidden objects inside baggage without opening them [5].…”

Section: Introductionmentioning

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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CH-Net: Deep adversarial autoencoders for semantic segmentation in X-ray images of cabin baggage screening at airports

Cited by 14 publications

References 22 publications

New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

IIE-SegNet: Deep Semantic Segmentation Network With Enhanced Boundary Based on Image Information Entropy

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

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