{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:27:39Z","timestamp":1743143259110,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819982950"},{"type":"electronic","value":"9789819982967"}],"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\/978-981-99-8296-7_5","type":"book-chapter","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:04:14Z","timestamp":1700179454000},"page":"64-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Vietnam\u2019s Motorcycle License Plate Detection and\u00a0Recognition Using Deep Learning Model"],"prefix":"10.1007","author":[{"given":"Duc Hoa","family":"Le","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Debarshi","family":"Mazumder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luyl-Da","family":"Quach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shreya","family":"Banerjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinh Dinh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic license plate recognition (ALPR): a state-of-theart review. IEEE Trans. Circuits Syst. Video Technol. 23(2), 311\u2013325 (2013)","DOI":"10.1109\/TCSVT.2012.2203741"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, G.R., Menotti, D., Schwartz, W.R.: License plate recognition based on temporal redundancy. In: IEEE 19th International Conference on Intelligent Transportation Systems (ITSC2016), pp. 2577\u20132582 (2016)","DOI":"10.1109\/ITSC.2016.7795970"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Rahman, C.A., Badawy, W., Radmanesh, A.: Deep automatic license a real time vehicle\u2019s license plate recognition system. In: IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS\u201903), pp. 163\u2013166 (2016)","DOI":"10.1109\/AVSS.2003.1217917"},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Nguyen, M.T.T., Nguyen, V.D., Jeon, J.W.: Real-time pedestrian detection using a support vector machine and stixel information. In: International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea (South), pp. 1350\u20131355 (2017). https:\/\/doi.org\/10.23919\/ICCAS.2017.8204203","DOI":"10.23919\/ICCAS.2017.8204203"},{"key":"5_CR5","doi-asserted-by":"publisher","unstructured":"Chau, D.H., et al.: Plant leaf diseases detection and identification using deep learning model. In: Hassanien, A.E., Rizk, R.Y., Snasel, V., AbdelKader, R.F. (eds.) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. LNDECT, vol. 113, pp. 3\u201310. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/9783031039188_1","DOI":"10.1007\/9783031039188_1"},{"key":"5_CR6","doi-asserted-by":"publisher","unstructured":"Nguyen, V.D., Trinh, T.D., Tran, H.N.: A robust triangular sigmoid pattern-based obstacle detection algorithm in resource-limited devices. IEEE Trans. Intell. Transp. Syst. 24(6), 5936\u20135945 (2023). https:\/\/doi.org\/10.1109\/TITS.2023.3253509","DOI":"10.1109\/TITS.2023.3253509"},{"key":"5_CR7","unstructured":"Badr A., Abdelwahab, M.M., Thabet, A.M., Abdelsadek, A.M.: Automatic number plate recognition system. Ann. Univ. Craiova - Math. Comput. Sci. Ser. 38(1), 62\u201371 (2011)"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Zheng, D., Zhao, Y., Wang, J.: An efficient method of license plate location. Pattern Recognit. Lett. 26(15), 2431\u20132438 (2005). https:\/\/doi.org\/10.1016\/j.patrec.2005.04.014","DOI":"10.1016\/j.patrec.2005.04.014"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic license plate recognition (ALPR): a state-of-the-art review. IEEE Trans. Circuits Syst. Video Technol. 23(2), 311\u2013325 (2013)","DOI":"10.1109\/TCSVT.2012.2203741"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Silva, S.M., Jung, C.R.: Real-time Brazilian license plate detection and recognition using deep convolutional neural networks. In: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 55\u201362, October 2017","DOI":"10.1109\/SIBGRAPI.2017.14"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"5_CR12","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics Yolov8 (2023). https:\/\/docs.ultralytics.com\/models\/Yolov8\/"},{"key":"5_CR13","unstructured":"Terven, J., Cordova-Esparza, D.: A comprehensive review of yolo: from yolov1 and beyond, 09 June 2023. arXiv. http:\/\/arxiv.org\/abs\/2304.00501. Accessed 05 July 2023"},{"key":"5_CR14","unstructured":"Motorcycle License Plate Detection - v8 2022\u201308-27 Roboflow. https:\/\/universe.roboflow.com\/motorcycle-9gyny\/motorcycle-license-plate-detection\/dataset\/8"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Aboah, A., Wang, B., Bagci, U., Adu-Gyamfi, Y.: Real-time multi-class helmet violation detection using few-shot data sampling technique and Yolov8, 13 April 2023. arXiv. http:\/\/arxiv.org\/abs\/2304.08256. Accessed 08 July 2023","DOI":"10.1109\/CVPRW59228.2023.00564"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"Duc, H.L., Minh, T.T., Hong, K.V., Hoang, H.L.: 84 Birds classification using transfer learning and EfficientNetB2. In: Dang, T.K., Kung, J., Chung, T.M. (eds.) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. CCIS, vol. 1688, pp. 698\u2013705. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-19-8069-5_50","DOI":"10.1007\/978-981-19-8069-5_50"},{"key":"5_CR17","doi-asserted-by":"publisher","unstructured":"Hong, K.V., Minh, T.T., Duc, H.L., Nhat, N.T., Hoang, H.L.: 104 Fruits classification using transfer learning and DenseNet201 fine-tuning. In: Barolli, L. (eds.) Complex, Intelligent and Software Intensive Systems. CISIS 2022. LNNS, vol. 497, pp. 160\u2013170. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-08812-4_16","DOI":"10.1007\/978-3-031-08812-4_16"},{"key":"5_CR18","doi-asserted-by":"publisher","unstructured":"Akhtar, Z., Ali, R.: Automatic number plate recognition using random forest classifier, 26 March 2023. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2303.14856","DOI":"10.48550\/arXiv.2303.14856"},{"key":"5_CR19","doi-asserted-by":"publisher","unstructured":"Neupane, D., Bhattarai, A., Aryal, S., Bouadjenek, M.R., Seok, U.-M., Seok, J.: SHINE: deep learning-based accessible parking management system, 28 April 2023. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2302.00837","DOI":"10.48550\/arXiv.2302.00837"},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Hatami, S., Sadedel, M., Jamali, F.: Iranian license plate recognition using a reliable deep learning approach, 03 May 2023. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2305.02292","DOI":"10.48550\/arXiv.2305.02292"},{"key":"5_CR21","unstructured":"MNIST Dataset $$>$$ Overview, Roboflow. https:\/\/universe.roboflow.com\/popular-benchmarks\/mnist-cjkff"},{"key":"5_CR22","doi-asserted-by":"publisher","unstructured":"Nguyen, D.V.M., Vu, A.T., Ross, V., Brijs, T., Wets, G., Brijs, K.: Small-displacement motorcycle crashes and risky ridership in Vietnam: findings from a focus group and in-depth interview study. Saf. Sci. 152, 105514 (2022). https:\/\/doi.org\/10.1016\/j.ssci.2021.105514","DOI":"10.1016\/j.ssci.2021.105514"}],"container-title":["Communications in Computer and Information Science","Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8296-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T03:37:15Z","timestamp":1730518635000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8296-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819982950","9789819982967"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8296-7_5","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":"17 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Da Nang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/thefdse.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":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","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":"38","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":"8","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":"28% - 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":"3","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":"6","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)"}}]}}