{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T09:21:18Z","timestamp":1780046478820,"version":"3.53.1"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819570836","type":"print"},{"value":"9789819570843","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-7084-3_11","type":"book-chapter","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T10:30:37Z","timestamp":1778236237000},"page":"167-183","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DEF-YOLO: Leveraging YOLO for\u00a0Concealed Weapon Detection in\u00a0Thermal Imaging"],"prefix":"10.1007","author":[{"given":"Divya","family":"Bhardwaj","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnav","family":"Ramamoorthy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Poonam","family":"Goyal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","unstructured":"Aharon, S., et al.: Super-gradients (2021). https:\/\/doi.org\/10.5281\/ZENODO.7789328","DOI":"10.5281\/ZENODO.7789328"},{"issue":"3","key":"11_CR2","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1007\/s10772-021-09822-2","volume":"25","author":"D Bhavana","year":"2022","unstructured":"Bhavana, D., Kishore Kumar, K., Ravi Tej, D.: Infrared and visible image fusion using latent low rank technique for surveillance applications. Int. J. Speech Technol. 25(3), 551\u2013560 (2022)","journal-title":"Int. J. Speech Technol."},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: YOLO-MS: rethinking multi-scale representation learning for real-time object detection. IEEE Trans. Pattern Anal. Mach. Intell. (2025)","DOI":"10.1109\/TPAMI.2025.3538473"},{"issue":"1","key":"11_CR4","doi-asserted-by":"publisher","first-page":"2735","DOI":"10.1038\/s41598-024-81054-1","volume":"15","author":"A Cheng","year":"2025","unstructured":"Cheng, A., Wu, S., Liu, X., Lu, H.: Enhancing concealed object detection in active THZ security images with adaptation-yolo. Sci. Rep. 15(1), 2735 (2025)","journal-title":"Sci. Rep."},{"issue":"1","key":"11_CR5","doi-asserted-by":"publisher","first-page":"12082","DOI":"10.1038\/s41598-022-16208-0","volume":"12","author":"L Cheng","year":"2022","unstructured":"Cheng, L., Ji, Y., Li, C., Liu, X., Fang, G.: Improved SSD network for fast concealed object detection and recognition in passive terahertz security images. Sci. Rep. 12(1), 12082 (2022)","journal-title":"Sci. Rep."},{"issue":"1","key":"11_CR6","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.1038\/s41598-024-53045-9","volume":"14","author":"R Cheng","year":"2024","unstructured":"Cheng, R., Lucyszyn, S.: Few-shot concealed object detection in sub-THZ security images using improved pseudo-annotations. Sci. Rep. 14(1), 3150 (2024)","journal-title":"Sci. Rep."},{"key":"11_CR7","unstructured":"Dwyer, B., Nelson, J., Hansen, T., et. al.: Roboflow (version 1.0) [software] (2024). https:\/\/roboflow.com.computervision"},{"key":"11_CR8","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)"},{"issue":"12","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.22214\/ijraset.2021.39506","volume":"9","author":"S Gosain","year":"2021","unstructured":"Gosain, S., Sonare, A., Wakodkar, S.: Concealed weapon detection using image processing and machine learning. Int. J. Res. Appl. Sci. Eng. Technol. 9(12), 1374\u20131384 (2021)","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Goyal, B., Dogra, A., Khoond, R., Gupta, A., Anand, R.: Infrared and visible image fusion for concealed weapon detection using transform and spatial domain filters. In: 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp.\u00a01\u20134. IEEE (2021)","DOI":"10.1109\/ICRITO51393.2021.9596074"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Hussein, N.J., Hu, F.: An alternative method to discover concealed weapon detection using critical fusion image of color image and infrared image. In: 2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI), pp. 378\u2013383. IEEE (2016)","DOI":"10.1109\/CCI.2016.7778947"},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.patrec.2016.12.011","volume":"94","author":"NJ Hussein","year":"2017","unstructured":"Hussein, N.J., Hu, F., He, F.: Multisensor of thermal and visual images to detect concealed weapon using harmony search image fusion approach. Pattern Recogn. Lett. 94, 219\u2013227 (2017)","journal-title":"Pattern Recogn. Lett."},{"key":"11_CR13","doi-asserted-by":"publisher","unstructured":"Jocher, G.: Ultralytics yolov5 (2020). https:\/\/doi.org\/10.5281\/zenodo.3908559. https:\/\/github.com\/ultralytics\/yolov5","DOI":"10.5281\/zenodo.3908559"},{"key":"11_CR14","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics yolov8 (2023). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"11_CR15","unstructured":"Jocher, G., Qiu, J.: Ultralytics yolo11 (2024). https:\/\/github.com\/ultralytics\/ultralytics"},{"issue":"1","key":"11_CR16","doi-asserted-by":"publisher","first-page":"8353","DOI":"10.1038\/s41598-024-56636-8","volume":"14","author":"W Khor","year":"2024","unstructured":"Khor, W., Chen, Y.K., Roberts, M., Ciampa, F.: Automated detection and classification of concealed objects using infrared thermography and convolutional neural networks. Sci. Rep. 14(1), 8353 (2024)","journal-title":"Sci. Rep."},{"key":"11_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.112995","volume":"310","author":"C Li","year":"2025","unstructured":"Li, C., Lyu, H., Duan, K.: A lightweight and efficient detector for concealed object in active millimeter wave images. Knowl.-Based Syst. 310, 112995 (2025)","journal-title":"Knowl.-Based Syst."},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Li, Y., Mao, H., Girshick, R., He, K.: Exploring plain vision transformer backbones for object detection. In: European Conference on Computer Vision, pp. 280\u2013296. Springer (2022)","DOI":"10.1007\/978-3-031-20077-9_17"},{"key":"11_CR19","unstructured":"Liang, D., Xue, F., Li, L.: Active terahertz imaging dataset for concealed object detection. arXiv preprint arXiv:2105.03677 (2021)"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"11_CR21","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision - ECCV 2014","author":"TY Lin","year":"2014","unstructured":"Lin, T.Y., et al.: Microsoft coco: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision - ECCV 2014, pp. 740\u2013755. Springer, Cham (2014)"},{"key":"11_CR22","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.engappai.2017.09.005","volume":"67","author":"S L\u00f3pez-Tapia","year":"2018","unstructured":"L\u00f3pez-Tapia, S., Molina, R., de la Blanca, N.P.: Using machine learning to detect and localize concealed objects in passive millimeter-wave images. Eng. Appl. Artif. Intell. 67, 81\u201390 (2018)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Miao, C., et al.: Sixray: a large-scale security inspection X-ray benchmark for prohibited item discovery in overlapping images. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00222"},{"issue":"3","key":"11_CR24","doi-asserted-by":"publisher","first-page":"72","DOI":"10.3390\/jimaging11030072","volume":"11","author":"JD Mu\u00f1oz","year":"2025","unstructured":"Mu\u00f1oz, J.D., Ruiz-Santaquiteria, J., Deniz, O., Bueno, G.: Concealed weapon detection using thermal cameras. J. Imaging 11(3), 72 (2025)","journal-title":"J. Imaging"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Raturi, G., Rani, P., Madan, S., Dosanjh, S.: Adocw: an automated method for detection of concealed weapon. In: 2019 Fifth International Conference on Image Information Processing (ICIIP), pp. 181\u2013186. IEEE (2019)","DOI":"10.1109\/ICIIP47207.2019.8985972"},{"key":"11_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2023.109303","volume":"216","author":"Y Su","year":"2024","unstructured":"Su, Y., et al.: Enhancing concealed object detection in active millimeter wave images using wavelet transform. Signal Process. 216, 109303 (2024)","journal-title":"Signal Process."},{"issue":"28","key":"11_CR27","doi-asserted-by":"publisher","first-page":"44259","DOI":"10.1007\/s11042-023-15358-1","volume":"82","author":"O Veranyurt","year":"2023","unstructured":"Veranyurt, O., Sakar, C.O.: Concealed pistol detection from thermal images with deep neural networks. Multimedia Tools Appl. 82(28), 44259\u201344275 (2023)","journal-title":"Multimedia Tools Appl."},{"key":"11_CR28","unstructured":"Wang, A., Chen, H., Liu, L., et\u00a0al.: Yolov10: real-time end-to-end object detection. arXiv preprint arXiv:2405.14458 (2024)"},{"key":"11_CR29","first-page":"51094","volume":"36","author":"C Wang","year":"2023","unstructured":"Wang, C., et al.: Gold-yolo: efficient object detector via gather-and-distribute mechanism. Adv. Neural. Inf. Process. Syst. 36, 51094\u201351112 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Self-paced feature attention fusion network for concealed object detection in millimeter-wave image. IEEE Trans. Circuits Syst. Video Technol. 32(1), 224\u2013239 (2021)","DOI":"10.1109\/TCSVT.2021.3058246"},{"key":"11_CR31","first-page":"1","volume":"71","author":"H Yang","year":"2022","unstructured":"Yang, H., Zhang, D., Hu, A., Liu, C., Cui, T.J., Miao, J.: Transformer-based anchor-free detection of concealed objects in passive millimeter wave images. IEEE Trans. Instrum. Meas. 71, 1\u201316 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets V2: more deformable, better results. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9308\u20139316 (2019)","DOI":"10.1109\/CVPR.2019.00953"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2025: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7084-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T10:30:43Z","timestamp":1778236243000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7084-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819570836","9789819570843"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7084-3_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wellington","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}