Optimizing Matrix Capsule Networks for Contraband detection Research

Zhiming Yan; Xinwei Li; Yi Yang

doi:10.62051/ijcsit.v2n1.34

Authors

Zhiming Yan
Xinwei Li
Yi Yang

DOI:

https://doi.org/10.62051/ijcsit.v2n1.34

Keywords:

Contraband classification; Matrix capsule network; Multi-feature extraction; Discarded Capsules

Abstract

An optimized matrix capsule network is proposed for the problem of contraband in parcels with different poses, different sizes, random occlusion and sample imbalance. The network improves the recognition accuracy with the help of the matrix capsule network's recognition ability for object poses, and is mainly composed of a multi-branch feature extraction network and a side branch matrix capsule network, which is used to extract large and small targets; the side branch matrix capsule network uses a larger capsule convolution kernel, which is capable of detecting larger targets, and uses the operation of randomly discarding the capsules in the side branch, which makes the parameter amount to be reduced while enhancing the learning ability of the network. The region of interest of the network is obtained by using heat map approach with the help of weight back propagation mapping back to the original map to localize the contraband. Through a large number of experiments on the SIXray dataset, it is proved that the network in this paper improves the detection accuracy by 9.43% and the processing speed of the model by about 1/3 compared with the original capsule network.

Downloads

Download data is not yet available.

References

WANG Yanqing, REN Jinjin, YU Qin. Impact Analysis of X-ray Machine Conveyor Belt Speed on Airport Security Missing Rate[J]. China Transportation Review, 2017, 39(05):55-59. (in Chinese)

ZHAO Zhenwu, WANG Junjie, ZHANG Yi. Research on reliability optimization of airport passenger security system[J]. China Safety Science Journal, 2023, 33(8): 182-189. (in Chinese)

STERCHI Y，HATTENSCHWILER N，MICHELS，et al. Relevance of visual inspection strategy and knowledge about everyday objects for X-ray baggage screening［C］// International Carnahan Conference on Security Technology，Oct. 23-26，2017，Ma⁃drid, Spain. IEEE, 2017:1-15.

YAN W，JING H. Object detection in X-ray images based on object candidate extraction and support vector machine［C］// Ninth International Conference on Natural Computation. May 19，2014，Shenyang，China. IEEE，2014：173-177.

Heitz G, Chechik G . Object separation in X-Ray image sets[C]// The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13-18 June 2010. IEEE, 2010.

Xiao Y, Wang S , Wang Z ,et al.Coordinated-security based on probabilistic shaping and encryption in MMW-RoF system.[J].Optics letters, 2023, 48 11:, 2989-2992. DOI:10.1364/ol.493644.

Mery D Automated detection in complex objects using a tracking algorithm in multiple X-ray views[C]//Computer Vision & Pattern Recognition Workshops.IEEE, 2011.DOI:10.1109/CVPRW.2011.5981715.

Hassan T, Akcay S , Bennamoun M, et al. Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion[C]//2020.DOI:10.1007/978-3-030-69544-6_16.

AKCAY S，KUNDEGORSKI M E，DEVEREUXM，et al. Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery［C］// 2016 IEEE International Conference on Image Processing，Sep. 25-28，Phoenix，AZ，USA. IEEE，2016：1057-1061.

Gao Qiang, Pan Jun, Hong Ruifeng. Research on automatic identification of dangerous goods in airport security check based on CNN[J]. Computer Technology and Development, 2019, 29(10):95-99. (in Chinese)

Yang J, Zhao Z , Zhang H ,et al.Data Augmentation for X-Ray Prohibited Item Images Using Generative Adversarial Networks[J].IEEE Access, 2019, PP(99):1-1.DOI:10.1109/ACCESS.2019.2902121.

Zhang Youkang, SU Zhigang, ZHANG Haigang, et al. X-Ray security images multiscale contraband detection[J]. Signal Processing, 2020, 36(7):11. (in Chinese)

Berg A C, Fu C Y , Szegedy C ,et al. SSD: Single Shot MultiBox Detector. 2015[2024-02-28]. DOI:10.1007/978-3-319-46448-0_2.

Miao C, Su C, Wan F, et al. SIXray: A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images.2019[2024-02-28].

He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[J]. IEEE, 2016. DOI:10.1109/CVPR.2016.90.

YOU Xi, HOU Jin, REN Dongsheng, et al. Adaptive Security Check Prohibited Items Detection Method with Fused Spatial Attention[J]. Computer Engineering and Applications, 2023, 59(21): 176-186.(in Chinese).

SU Xingwang, Wang Xiaoming, Huang Jinbo, et al. X-ray security contraband detection based on deformable convolution and attention mechanism[J]. Electronic Measurement Technology, 2023, 46(10): 98-108. (in Chinese)

LI Song, Yasenjiang Musa. Improved YOLOv7 X-Ray Image Real-Time Detection of Prohibited Items[J]. Computer Engineering and Applications, 2023, 59(12): 193-200. (in Chinese)

SABOUR S，FROSST N，HINTON G E. Dynamic routing between capsules［C］// 31th Neural Information Processing Systems，Dec. 4-9, 2017, Long beach, CA, USA. NIPS Proceeding, 2017:3856-3866.

Hinton, G.E., Sabour, S., Frosst, N.: Matrix capsules with EM routing. In: International Conference on Learning Representations (2018)

Gao Xin, Yu Jiahao, Zha Sen, Fu Shiyuan, Xue Bing, Ye Ping, Huang Zijian, Zhang Guangyao. An ensemble-based outlier detection method for clustered and local outliers with differential potential spread loss[J]. Knowledge-Based Systems,2022,258.

Szegedy C, Liu W Jia Y Q et al Going deeper with convolutions C ∥ 2015 IEEE Conference on Computer Vision and Pattern Recognition CVPR June7-12 2015 Boston MA USA New York IEEE 2015 15523970

SELVARAJU R R，COGSWELL M，DAS A，et al. Grad-CAM：Visual explanations from deep net works via gradient-based localization［C］// International Conference on Computer Vision，Oct.22-29，2017，Venice，Italy. IEEE，2017：618-626.

HUANG G，LIU Z，LAURENS V，et al. Densely connected convolutional networks［C］// 2017 IEEE Conference on Computer Vision and Pattern Recognition, Jul. 21-26, 2017, Honolulu, HI, USA.IEEE, 2016:2261-2269.

He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep Residual Learning for Image Recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770–778.

SZEGEDY C，VANHOUCKE V，IOFFE S，et al. Rethinking the inception architecture for computer vision［C］// 2016 IEEE Conference on Computer Vision and Pattern Recognition，Jun. 27-30，2016，Las Vegas，NV，USA.IEEE，2016：2818-2826.

Howard A, Sandler M , Chu G ,et al.Searching for MobileNetV3[J]. 2019.DOI:10.48550/arXiv.1905.02244.

Tan M, Le Q V .EfficientNetV2: Smaller Models and Faster Training[J]. 2021.DOI:10.48550/arXiv.2104.00298.

Mery, Domingo, et al. "GDXray: The database of X-ray images for nondestructive testing." Journal of Nondestructive Evaluation 34 (2015): 1-12.