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Current Chinese Computer Science

Editor-in-Chief

ISSN (Print): 2665-9972
ISSN (Online): 2665-9964

Research Article

Progressive Image Recognition Method and Its Application in Security Inspection Machines

Author(s): Wu Jianxing, Zeng Dexin, Ju Qiaodan, Chang Zixuan and Yu Hai*

Volume 1, Issue 1, 2021

Published on: 08 December, 2020

Page: [52 - 60] Pages: 9

DOI: 10.2174/2665997201999201208210819

Abstract

Background: Owing to the ability of a deep learning algorithm to identify objects and the related detection technology of security inspection equipment, in this paper, we propose a progressive object recognition method that considers local information of objects.

Methods: First, we construct an X-Base model by cascading multiple convolutions and pooling layers to obtain the feature mapping image. Moreover, we provide a “segmented convolution, unified recognition” strategy to detect the size of the objects.

Results: Experimental results show that this method can effectively identify the specifications of bags passing through the security inspection equipment. Compared with the traditional VGG and progressive VGG recognition methods, the proposed method achieves advantages in terms of efficiency and concurrency.

Conclusion: This study provides a method to gradually recognize objects and can potentially assist the operators in identifying prohibited objects.

Keywords: Progressive image recognition, convolutional neural network, security inspection, algorithm, X-ray, deep learning.

Graphical Abstract
[1]
B. Knut, S. Uwe, T. Helmut, and N. Dirk, "Device and method for inspection of baggage and other objects", U.S. P 1 262 798.
[2]
D. Mery, E. Svec, M. Arias, V. Riffo, J. Saavedra, and S. Banerjee, "Modern computer vision techniques for x-ray testing in baggage inspection", IEEE Trans. Syst. Man Cybern. Syst., vol. 47, no. 4, pp. 682-693, 2017.
[http://dx.doi.org/10.1109/TSMC.2016.2628381]
[3]
D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Leaning internal representations by back-propagating errors", Nature, vol. 323, no. 6088, pp. 318-362, 1986.
[http://dx.doi.org/10.1038/323533a0]
[4]
G.E. Hinton, S. Osindero, and Y-W. Teh, "A fast learning algorithm for deep belief nets", Neural Comput., vol. 18, no. 7, pp. 1527-1554, 2006.
[http://dx.doi.org/10.1162/neco.2006.18.7.1527 PMID: 16764513]
[5]
S. Zhang, Y. Gong, and J. Wang, "The development of deep convolution neural network and its applications on computer vision", Chinese J. Comp., vol. 42, no. 3, pp. 453-482, 2019.
[6]
H. Zheng, J. Fu, and T. Mei, "Learning multi-attention convolutional neural network for fine-grained image recognition", ICCV, Venice, Italy, pp. 5217-5227, 2017.
[http://dx.doi.org/10.1109/ICCV.2017.557]
[7]
D. Cirean, U. Meier, and J. Schmidhuber, "Multi-column deep neural networks for image classification", CVPR, RI, USA, pp. 3642-3649, 2012.
[8]
D. Mery, D. Saavedra, and M. Prasad, "x-ray baggage inspection with computer vision: a survey", IEEE Access, vol. 8, pp. 145620-145633, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3015014]
[9]
D. Mery, "X-ray testing by computer vision", CVPRW. IEEE: Portland, OR, pp. 23-28, 2013.
[http://dx.doi.org/10.1109/CVPRW.2013.61]
[10]
D. Mery, and C. Arteta, "Automatic defect recognition in x-ray testing using computer vision", WACV. IEEE: Santa Rosa, CA, pp. 24-31, 2017.
[http://dx.doi.org/10.1109/WACV.2017.119]
[11]
K. He, X. Zhang, and S. Ren, "Spatial pyramid pooling in deep convolutional networks for visual recognition", ECCV, Zurich, Switzerland, pp. 1904-1916, 2014.
[http://dx.doi.org/10.1007/978-3-319-10578-9_23]
[12]
K. He, X. Zhang, and S. Ren, "Deep residual learning for image recognition", CVPR, USA, pp. 770-778, 2015.
[13]
X. Zhang, J. Zou, K. He, and J. Sun, "Accelerating very deep convolutional networks for classification and detection", IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 10, pp. 1943-1955, 2016.
[http://dx.doi.org/10.1109/TPAMI.2015.2502579 PMID: 26599615]
[14]
Y. Gong, L. Wang, R. Guo, and S. Lazebnik, Multi-scale Orderless Pooling of Deep Convolutional Activation Features.ECCV. Springer: Cham, pp. 392-407, 2014.
[15]
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, and V. Vanhoucke, A rabinovich, going deeper with convolutions.CVPR., IEEE: Boston, MA, 2015.
[16]
K. Simonyan, and A. Zisserman, very deep convolutional networks for large-scale image recognition., ICLR: U.K, 2015.
[17]
C. Miao, L. Xie, F. Wan, C. Su, H. Liu, J. Jiao, and Q. Ye, "SIX ray: a large-scale security inspection x-ray benchmark for prohibited item discovery in overlapping images", CVPR. IEEE: Long Beach, CA, pp. 2119-2128, 2019.
[http://dx.doi.org/10.1109/CVPR.2019.00222]
[18]
D. Mery, V. Riffo, U. Zscherpel, G. Mondragon, I. Lillo, I. Zuccar, H. Lobel, and M. Carrasco, "GDXray: The database of X-ray images for nondestructive testing", J. Nondestructive Evaluation, vol. 34, no. 4, pp. 1-12, 2015.
[19]
G.R. Bradski, and A. Kaehler, Learning OpenCV - computer vision with the OpenCV library: software that sees.
[20]
V. Vapnik, Statistical learning theory., USA, 1998.

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