{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T14:30:17Z","timestamp":1777991417395,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Safety Capacity Building Fund Project of Civil Aviation Administration of China","award":["KLZ49420200019"],"award-info":[{"award-number":["KLZ49420200019"]}]},{"name":"Safety Capacity Building Fund Project of Civil Aviation Administration of China","award":["KLZ49420200023"],"award-info":[{"award-number":["KLZ49420200023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, automatic detection of threats in X-ray baggage has become important in security inspection. However, the training of threat detectors often requires extensive, well-annotated images, which are hard to procure, especially for rare contraband items. In this paper, a few-shot SVM-constraint threat detection model, named FSVM is proposed, which aims at detecting unseen contraband items with only a small number of labeled samples. Rather than simply finetuning the original model, FSVM embeds a derivable SVM layer to back-propagate the supervised decision information into the former layers. A combined loss function utilizing SVM loss is also created as the additional constraint. We have evaluated FSVM on the public security baggage dataset SIXray, performing experiments on 10-shot and 30-shot samples under three class divisions. Experimental results show that compared with four common few-shot detection models, FSVM has the highest performance and is more suitable for complex distributed datasets (e.g., X-ray parcels).<\/jats:p>","DOI":"10.3390\/s23084069","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T05:18:13Z","timestamp":1681795093000},"page":"4069","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["FSVM: A Few-Shot Threat Detection Method for X-ray Security Images"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2696-2721","authenticated-orcid":false,"given":"Cheng","family":"Fang","sequence":"first","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7104-4874","authenticated-orcid":false,"given":"Jiayue","family":"Liu","sequence":"additional","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Han","sequence":"additional","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingrui","family":"Chen","sequence":"additional","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1778-8645","authenticated-orcid":false,"given":"Dayu","family":"Liao","sequence":"additional","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Michel, S., Koller, S.M., de Ruiter, J.C., Moerland, R., Hogervorst, M., and Schwaninger, A. (2007, January 8\u201311). Computer-based training increases efficiency in X-ray image interpretation by aviation security screeners. Proceedings of the 2007 41st Annual IEEE International Carnahan Conference on Security Technology, Ottawa, ON, Canada.","DOI":"10.1109\/CCST.2007.4373490"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 14\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108245","DOI":"10.1016\/j.patcog.2021.108245","article-title":"Towards automatic threat detection: A survey of advances of deep learning within X-ray security imaging","volume":"122","author":"Akcay","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_4","first-page":"850","article-title":"Large margin deep networks for classification","volume":"31","author":"Elsayed","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., and Liu, W. (2018, January 18\u201322). Cosface: Large margin cosine loss for deep face recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00552"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13007-022-00866-2","article-title":"A survey of few-shot learning in smart agriculture: Developments, applications, and challenges","volume":"18","author":"Yang","year":"2022","journal-title":"Plant Methods"},{"key":"ref_7","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017, January 6\u201311). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_8","unstructured":"Ravi, S., and Larochelle, H. (2017, January 24\u201326). Optimization as a model for few-shot learning. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, P., Bai, Y., Wang, D., Bai, B., and Li, Y. (2020). Few-shot classification of aerial scene images via meta-learning. Remote Sens., 13.","DOI":"10.20944\/preprints202010.0033.v1"},{"key":"ref_10","first-page":"5611711","article-title":"Scale-aware detailed matching for few-shot aerial image semantic segmentation","volume":"60","author":"Yao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cai, J., Zhang, Y., Guo, J., Zhao, X., Lv, J., and Hu, Y. (2022). St-pn: A spatial transformed prototypical network for few-shot sar image classification. Remote Sens., 14.","DOI":"10.3390\/rs14092019"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019). Deep transfer learning for few-shot SAR image classification. Remote Sens., 11.","DOI":"10.20944\/preprints201905.0030.v1"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Paul, A., Tang, Y.X., and Summers, R.M. (2020, January 11\u201316). Fast few-shot transfer learning for disease identification from chest X-ray images using autoencoder ensemble. Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, Orlando, FL, USA.","DOI":"10.1117\/12.2549060"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cherti, M., and Jitsev, J. (2021). Effect of Pre-Training Scale on Intra-and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-ray Chest Images. arXiv.","DOI":"10.1109\/IJCNN55064.2022.9892393"},{"key":"ref_15","unstructured":"Singh, M., and Singh, S. (April, January 31). Optimizing image enhancement for screening luggage at airports. Proceedings of the CIHSPS 2005. Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, Orlando, FL, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1109\/TIFS.2018.2812196","article-title":"Using deep convolutional neural network architectures for object classification and detection within X-ray baggage security imagery","volume":"13","author":"Akcay","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_17","first-page":"17","article-title":"Deep CMST framework for the autonomous recognition of heavily occluded and cluttered baggage items from multivendor security radiographs","volume":"14","author":"Hassan","year":"2019","journal-title":"CoRR"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhang, H., and Yang, J. (2018, January 23\u201326). Prohibited item detection in airport X-ray security images via attention mechanism based CNN. Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Guangzhou, China.","DOI":"10.1007\/978-3-030-03335-4_37"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3519022","article-title":"Few-shot object detection: A survey","volume":"54","author":"Antonelli","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_20","unstructured":"Wang, X., Huang, T.E., Darrell, T., Gonzalez, J.E., and Yu, F. (2020). Frustratingly simple few-shot object detection. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, B., Li, B., Cai, S., Yuan, Y., and Zhang, C. (2021, January 19\u201325). Fsce: Few-shot object detection via contrastive proposal encoding. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.00727"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J.B., and Isola, P. (2020, January 23\u201328). Rethinking few-shot image classification: A good embedding is all you need?. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"ref_23","unstructured":"Long, M., Cao, Y., Wang, J., and Jordan, M. (2015, January 6\u201311). Learning transferable features with deep adaptation networks. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hoffer, E., and Ailon, N. (2015, January 12\u201314). Deep metric learning using triplet network. Proceedings of the International Workshop on Similarity-Based Pattern Recognition, Copenhagen, Denmark.","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, P., Xie, J., Wang, Q., and Zuo, W. (2017, January 22\u201329). Is second-order information helpful for large-scale visual recognition?. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.228"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, Q., Li, P., and Zhang, L. (2017, January 21\u201326). G2DeNet: Global Gaussian distribution embedding network and its application to visual recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.689"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., and Soatto, S. (2019, January 15\u201320). Meta-learning with differentiable convex optimization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sun, B., Feng, J., and Saenko, K. (2016, January 12\u201317). Return of frustratingly easy domain adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, Arizona.","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6891","DOI":"10.1007\/s00521-020-05465-7","article-title":"Robust and high-order correlation alignment for unsupervised domain adaptation","volume":"33","author":"Cheng","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A comprehensive survey on support vector machine classification: Applications, challenges and trends","volume":"408","author":"Cervantes","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_31","first-page":"265","article-title":"On the algorithmic implementation of multiclass kernel-based vector machines","volume":"2","author":"Crammer","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","unstructured":"Barratt, S. (2018). On the differentiability of the solution to convex optimization problems. arXiv."},{"key":"ref_33","unstructured":"Amos, B., and Kolter, J.Z. (2017, January 7\u20139). Optnet: Differentiable optimization as a layer in neural networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_34","first-page":"1137","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Miao, C., Xie, L., Wan, F., Su, C., Liu, H., Jiao, J., and Ye, Q. (2019, January 15\u201320). Sixray: A large-scale security inspection X-ray benchmark for prohibited item discovery in overlapping images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00222"},{"key":"ref_36","unstructured":"Yan, X., Chen, Z., Xu, A., Wang, X., Liang, X., and Lin, L. (November, January 27). Meta r-cnn: Towards general solver for instance-level low-shot learning. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xiao, Y., and Marlet, R. (2020, January 23\u201328). Few-shot object detection and viewpoint estimation for objects in the wild. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58520-4_12"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/4069\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:18:01Z","timestamp":1760123881000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/4069"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,18]]},"references-count":38,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23084069"],"URL":"https:\/\/doi.org\/10.3390\/s23084069","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,18]]}}}