{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T15:56:16Z","timestamp":1764172576709,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819916474"},{"type":"electronic","value":"9789819916481"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-1648-1_46","type":"book-chapter","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T07:02:39Z","timestamp":1681455759000},"page":"553-566","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Firearm Detection in\u00a0Images and\u00a0Videos Using YOLO-Based Model"],"prefix":"10.1007","author":[{"given":"Sourav","family":"Mishra","sequence":"first","affiliation":[]},{"given":"Vijay K.","family":"Chaurasiya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,15]]},"reference":[{"key":"46_CR1","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1113\/jphysiol.1968.sp008455","volume":"195","author":"DH Hubel","year":"1968","unstructured":"Hubel, D.H., Wiesel, T.N.: Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195, 215\u2013243 (1968)","journal-title":"J. Physiol."},{"key":"46_CR2","unstructured":"Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001)"},{"key":"46_CR3","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"46_CR4","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. Microsoft Research (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"46_CR5","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks (2015)"},{"key":"46_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. Facebook AI Research (FAIR) (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"46_CR7","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified. real-time object detection University of Washington, Allen Institute for AI, Facebook AI Research (2015)","DOI":"10.1109\/CVPR.2016.91"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Grega, M., Matiola\u0144ski, A., Guzik, P., Leszczuk, M.: Automated detection of firearms and knives in a CCTV image (2015)","DOI":"10.3390\/s16010047"},{"key":"46_CR9","doi-asserted-by":"crossref","unstructured":"Egiazarov, A., Mavroeidis, V., Zennaro, F.M., Vishi, K.: Firearm detection and segmentation using an ensemble of semantic neural networks. Digital Security Group University of Oslo (2020)","DOI":"10.1109\/EISIC49498.2019.9108871"},{"key":"46_CR10","unstructured":"Dataset. https:\/\/www.kaggle.com\/atulyakumar98\/gundetection"},{"key":"46_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/978-3-319-59063-9_4","volume-title":"Artificial Intelligence and Soft Computing","author":"A Fuentes","year":"2017","unstructured":"Fuentes, A., Im, D.H., Yoon, S., Park, D.S.: Spectral analysis of CNN for tomato disease identification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 40\u201351. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59063-9_4"},{"key":"46_CR12","doi-asserted-by":"crossref","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning, pp. 7\u20139 (2019)","DOI":"10.1186\/s40537-019-0197-0"},{"key":"46_CR13","unstructured":"Dataset. https:\/\/www.edgecase.ai\/articles\/worlds-first-synthetic-gun-detection-dataset-from-edgecase-ai"},{"issue":"9","key":"46_CR14","doi-asserted-by":"publisher","first-page":"2203","DOI":"10.1109\/TIFS.2018.2812196","volume":"13","author":"S Akcay","year":"2018","unstructured":"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. For. Secur. 13(9), 2203\u20132215 (2018)","journal-title":"IEEE Trans. Inf. For. Secur."},{"key":"46_CR15","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.neucom.2017.05.012","volume":"275","author":"R Olmos","year":"2018","unstructured":"Olmos, R., Tabik, S., Herrera, F.: Automatic handgun detection alarm in videos using deep learning. Neurocomputing 275, 66\u201372 (2018)","journal-title":"Neurocomputing"},{"key":"46_CR16","doi-asserted-by":"publisher","unstructured":". Warsi, A., Abdullah, M., Husen, M.N., Yahya, M., Khan, S., Jawaid, N.: Gun detection system using Yolov3. In: 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), pp. 1\u20134 (2019). https:\/\/doi.org\/10.1109\/ICSIMA47653.2019.9057329","DOI":"10.1109\/ICSIMA47653.2019.9057329"},{"key":"46_CR17","doi-asserted-by":"publisher","unstructured":"Kumar, B., Punitha, R., Mohana, C.: YOLOv3 and YOLOv4: multiple object detection for surveillance applications. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 1316\u20131321 (2020). https:\/\/doi.org\/10.1109\/ICSSIT48917.2020.9214094","DOI":"10.1109\/ICSSIT48917.2020.9214094"},{"key":"46_CR18","doi-asserted-by":"publisher","first-page":"34366","DOI":"10.1109\/ACCESS.2021.3059170","volume":"9","author":"MT Bhatti","year":"2021","unstructured":"Bhatti, M.T., Khan, M.G., Aslam, M., Fiaz, M.J.: Weapon detection in real-time CCTV videos using deep learning. IEEE Access 9, 34366\u201334382 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3059170","journal-title":"IEEE Access"},{"key":"46_CR19","unstructured":"Brownlee, J.: A Gentle Introduction to Transfer Learning for Deep Learning (2017). https:\/\/machinelearningmastery.com\/transferlearning-for-deep-learning\/"},{"key":"46_CR20","unstructured":"Evolution-of-Yolo. https:\/\/towardsdatascience.com\/evolution-of-yolo-yolo-version-1-afb8af302bd2"},{"key":"46_CR21","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst., 2672\u20132680 (2014)"},{"key":"46_CR22","unstructured":"Lin, M., Chen, Q., Yan, S.: Network In Network (2013). arXiv:1312.4400"},{"key":"46_CR23","unstructured":"An open source dataset under Creative Commons Attribution-ShareAlike 4.0 International License. https:\/\/github.com\/ari-dasci\/OD-WeaponDetection"},{"key":"46_CR24","unstructured":"Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition (2015). arXiv:1409.1556"},{"key":"46_CR25","doi-asserted-by":"publisher","unstructured":". Hashmi, T.S.S., Haq, N.U., Fraz, M.M., Shahzad, M.: Application of deep learning for weapons detection in surveillance videos. In: 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), pp. 1\u20136 (2021). https:\/\/doi.org\/10.1109\/ICoDT252288.2021.9441523","DOI":"10.1109\/ICoDT252288.2021.9441523"},{"key":"46_CR26","unstructured":"India sees the third-highest firearm-related deaths in the world by Joe Myers https:\/\/theprint.in\/india\/india-sees-the-third-highest-firearm-related-deaths-in-the-world\/274576"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-1648-1_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T07:20:36Z","timestamp":1681456836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-1648-1_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819916474","9789819916481"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-1648-1_46","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"359","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.65","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}