journal article·2023-06

MC-CDPNet: Multi-Channel Correlated Detail Preserving Network for X-Ray-Based Baggage Screening

Dioline Sara, Ajay Kumar Mandava

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Advanced Neural Network Applications · Advanced X-ray and CT Imaging · Adversarial Robustness in Machine Learning

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (37)

Show source-provided references
1. Zentai, G.: X-ray imaging for homeland security. Int. J. Signal Imaging Syst. Eng. 3(1), 13–20 (2010)10.1504/IJSISE.2010.034628 ↗
2. Michel, S., Koller, S.M., de Ruiter, J.C., Moerland, R., Hogervorst, M., Schwaninger, A.: Computer-based training increases efficiency in X-ray image interpretation by aviation security screeners. In 2007 41st Annual IEEE international Carnahan conference on security technology, 201–206. (2007)10.1109/CCST.2007.4373490 ↗
3. Barrientos, J.M.: Screener threat detection performance with three dimensional X-Ray imaging. In: Proceedings IEEE 34th Annual 2000 International Carnahan Conference on Security Technology (Cat. No. 00CH37083) 178–191. (2000)10.1109/CCST.2000.891185 ↗
4. Bastan, M., Byeon, W., Breuel, T.M.: Object recognition in Multi-View Dual Energy X-ray images. InBMVC. 1(2), 11 (2013)
5. Turcsany, D., Mouton, A., Breckon, T.P.: Improving feature-based object recognition for X-ray baggage security screening using primed visualwords. In: 2013 IEEE International conference on industrial technology (ICIT) 1140–1145. (2013)10.1109/ICIT.2013.6505833 ↗
6. Hu, B., Zhang, C., Wang, L., Zhang, Q., Liu, Y.: Multi-label x-ray imagery classification via bottom-up attention and meta fusion. In: Proceedings of the Asian Conference on Computer Vision. (2020)10.1007/978-3-030-69544-6_11 ↗
7. Chaurasia, A., Culurciello, E.: Linknet: Exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE visual communications and image processing 1–4. (2017)10.1109/VCIP.2017.8305148 ↗
8. Wang, Y., Huang, J.: Object detection in x-ray images based on object candidate extraction and support vector machine. In: 2013 Ninth International Conference on Natural Computation (ICNC) 173–177. (2013)10.1109/ICNC.2013.6817965 ↗
9. Dmitruk, K., Denkowski, M., Mazur, M., Mikołajczak, P.: Sharpening filter for false color imaging of dual-energy X-ray scans. Signal. Image Video Process. 11, 613–620 (2017)10.1007/s11760-016-1001-7 ↗
10. Baştan, M., Yousefi, M.R., Breuel, T.M.: Visual words on baggage X-ray images. In: Computer Analysis of Images and Patterns: 14th International Conference, CAIP 2011, Seville, Spain, August 29–31, 2011, Proceedings, Part I 2011 pp. 360–368. (2011)10.1007/978-3-642-23672-3_44 ↗
11. Mery, D., Svec, E., Arias, M.: Object recognition in X-ray testing using adaptive sparse representations. J. Nondestr. Eval. 35, 1–9 (2016)10.1007/s10921-016-0362-8 ↗
12. Franzel, T., Schmidt, U., Roth, S.: Object detection in multi-view X-ray images. In: Pattern Recognition: Joint 34th DAGM and 36th OAGM Symposium, Graz, Austria, August 28–31, 2012. Proceedings vol. 34, pp. 144–154. (2012)10.1007/978-3-642-32717-9_15 ↗
13. Baştan, M.: Multi-view object detection in dual-energy X-ray images. Mach. Vis. Appl. 26(7–8), 1045–1060 (2015)10.1007/s00138-015-0706-x ↗
14. Mery, D., Riffo, V.: Automated detection of threat objects using adapted implicit shape model. IEEE Trans. Syst. ManCybern Syst. 46, 472–482 (2016)10.1109/TSMC.2015.2439233 ↗
15. Riffo, V., Godoy, I., Mery, D.: Handgun detection in single-spectrum multiple X-ray views based on 3d object recognition. J. Nondestr. Eval. 38, 1–1 (2019)10.1007/s10921-019-0602-9 ↗
16. Akçay, S., Kundegorski, M.E., Devereux, M., Breckon, T.P.: Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery. In2016 IEEE International Conference on Image Processing (ICIP) 1057–1061. (2016)10.1109/ICIP.2016.7532519 ↗
17. Akcay, S., Kundegorski, M.E., Willcocks, C.G., Breckon, T.P.: Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery. IEEE Trans. Inf. Forensics Secur. 13(9), 2203–2215 (2018)10.1109/TIFS.2018.2812196 ↗
18. Mithal, A., Baser, M.: Automatic Threat Detection in Baggage Security Imagery using Deep Learning Models. In: 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) 180–185. (2020)10.1109/ICIIS51140.2020.9342691 ↗
19. Benedykciuk, E., Denkowski, M., Dmitruk, K.: Material classification in X-ray images based on multi-scale CNN. Signal, Image and Video Processing 1–9. (2021)10.1007/s11760-021-01859-9 ↗
20. Hassan, T., Bettayeb, M., Akçay, S., Khan, S., Bennamoun, M., Werghi, N.: Detecting prohibited items in X-ray images: A contour proposal learning approach. In: 2020 IEEE International Conference on Image Processing (ICIP) 2016–2020. (2020)10.1109/ICIP40778.2020.9190711 ↗
21. Hassan, T., Shafay, M., Akçay, S., Khan, S., Bennamoun, M., Damiani, E., Werghi, N.: Meta-transfer learning driven tensor-shot detector for the autonomous localization and recognition of concealed baggage threats. Sensors. 20(22), 6450 (2020)10.3390/s20226450 ↗
22. Saavedra, D., Banerjee, S., Mery, D.: Detection of threat objects in baggage inspection with X-ray images using deep learning. Neural Comput. Appl. 33, 7803–7819 (2021)10.1007/s00521-020-05521-2 ↗
23. Wang, M., Du, H., Mei, W., Yuan, D.: Weight-guided dual-direction-fusion feature pyramid network for prohibited item detection in x-ray images. J. Electron. Imaging. 31(3), 033032 (2022)10.1117/1.JEI.31.3.033032 ↗
24. Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV) 3–19. (2018)10.1007/978-3-030-01234-2_1 ↗
25. An, J., Zhang, H., Zhu, Y., Yang, J.: Semantic segmentation for prohibited items in baggage inspection. In: Intelligence Science and Big Data Engineering. Visual Data Engineering: 9th International Conference, IScIDE 2019, Nanjing, China, October 17–20, 2019, Proceedings, Part I 9 pp. 495–505. (2019)10.1007/978-3-030-36189-1_41 ↗
26. Hassan, T., Akçay, S., Bennamoun, M., Khan, S., Werghi, N.: Unsupervised anomaly instance segmentation for baggage threat recognition. J. Ambient. Intell. Humaniz. Comput. 14, 1607–1618 (2023). https://doi.org/10.1007/s12652-021-03383-710.1007/s12652-021-03383-7 ↗
27. Hassan, T., Akcay, S., Bennamoun, M., Khan, S., Werghi, N.: A novel incremental learning driven instance segmentation framework to recognize highly cluttered instances of the contraband items. IEEE Trans. Syst. Man Cybernetics: Syst. 52(11), 6937–6951 (2021)10.1109/TSMC.2021.3131421 ↗
28. Huang, Z., Huang, L., Gong, Y., Huang, C., Wang, X.: Mask scoring r-cnn. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 6409–6418. (2019)10.1109/CVPR.2019.00657 ↗
29. He, K., Gkioxari, G., Dollár, P., Girshick, R., Mask: r-cnn In: Proceedings of the IEEE international conference on computer vision 2961–2969. (2017)10.1109/ICCV.2017.322 ↗
30. Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., Ouyang, W., Loy, C.C.: Hybrid task cascade for instance segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 4974–4983. (2019)10.1109/CVPR.2019.00511 ↗
31. Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: Yolact: Real-time instance segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision 9157–9166. (2019)10.1109/ICCV.2019.00925 ↗
32. Wang, L., Li, D., Zhu, Y., Tian, L., Shan, Y.: Dual super-resolution learning for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 3774–3783. (2020)10.1109/CVPR42600.2020.00383 ↗
33. Akilan, T., Wu, Q.J., Zhang, W.: Video foreground extraction using multi-view receptive field and encoder–decoder DCNN for traffic and surveillance applications. IEEE Trans. Veh. Technol. 68(10), 9478–9493 (2019)10.1109/TVT.2019.2937076 ↗
34. Hassan, T., Werghi, N.: Trainable structure tensors for autonomous baggage threat detection under extreme occlusion. InProceedings of the Asian Conference on Computer Vision. (2020)10.1007/978-3-030-69544-6_16 ↗
35. Miao, C., Xie, L., Wan, F., Su, C., Liu, H., Jiao, J., Ye, Q.: Sixray: a large-scale security inspection x-ray benchmark for prohibited item discovery in overlapping images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2119–2128. (2019)10.1109/CVPR.2019.00222 ↗
36. Mery, D., Riffo, V., Zscherpel, U., Mondragón, G., Lillo, I., Zuccar, I., Lobel, H., Carrasco, M.: GDXray: The database of X-ray images for nondestructive testing. J. Nondestr. Eval. 34, 1–2 (2015)10.1007/s10921-015-0315-7 ↗
37. Hassan, T., Akçay, S., Bennamoun, M., Khan, S., Werghi, N.: Tensor pooling-driven instance segmentation framework for baggage threat recognition. Neural Computing and Applications 1–2. (2022)10.1007/s00521-021-06411-x ↗