{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:40:24Z","timestamp":1743115224233,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031314162"},{"type":"electronic","value":"9783031314179"}],"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\/978-3-031-31417-9_32","type":"book-chapter","created":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T12:02:31Z","timestamp":1683374551000},"page":"415-427","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Traffic Sign Detection and\u00a0Recognition Using Dense Connections in\u00a0YOLOv4"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8062-7919","authenticated-orcid":false,"given":"Swastik","family":"Saxena","sequence":"first","affiliation":[]},{"given":"Somnath","family":"Dey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,7]]},"reference":[{"key":"32_CR1","doi-asserted-by":"crossref","unstructured":"Gomez-Moreno, H., Maldonado-Bascon, S., Gil-Jimenez, P., Lafuente-Arroyo, S.: Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition, In: IEEE Transactions on Intelligent Transportation Systems 11, pp. 917\u2013930 (2010)","DOI":"10.1109\/TITS.2010.2054084"},{"issue":"1","key":"32_CR2","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1016\/j.patcog.2009.05.018","volume":"43","author":"A Ruta","year":"2010","unstructured":"Ruta, A., Li, Y., Liu, X.: Real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recogn. 43(1), 416\u2013430 (2010)","journal-title":"Pattern Recogn."},{"key":"32_CR3","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1016\/j.patcog.2014.05.017","volume":"48","author":"S Salti","year":"2015","unstructured":"Salti, S., Petrelli, A., Tombari, F., Fioraio, N., Stefano, L.D.: Traffic sign detection via interest region extraction. Pattern Recogn. 48, 1039\u20131049 (2015)","journal-title":"Pattern Recogn."},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Nguwi, Y.-Y., Kouzani, A.: Automatic Road Sign Recognition Using Neural Networks, In: The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 3955\u20133962, (2006)","DOI":"10.1109\/IJCNN.2006.246916"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Romdhane, N.B., Mliki, H., Hammami, M.: An improved traffic signs recognition and tracking method for driver assistance system In: 2016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS), pp. 1\u20136 (2016)","DOI":"10.1109\/ICIS.2016.7550772"},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Yakimov, P., Fursov, V.: Traffic Signs Detection and tracking using modified Hough transform, In: 2015 12th International Joint Conference on e-Business and Telecommunications (ICETE), pp. 22\u201328 (2015)","DOI":"10.5220\/0005543200220028"},{"key":"32_CR7","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1109\/TITS.2008.922935","volume":"9","author":"N Barnes","year":"2008","unstructured":"Barnes, N., Zelinsky, A., Fletcher, L.S.: Real-Time speed sign detection using the Radial symmetry detector. IEEE Trans. Intell. Transp. Syst. 9, 322\u2013332 (2008)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhang, H., Wang, B., Gao, Z.: Robust traffic sign recognition and tracking for advanced driver assistance systems, In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 704\u2013709 (2012)","DOI":"10.1109\/ITSC.2012.6338799"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation (2014) arXiv [cs.CV]","DOI":"10.1109\/CVPR.2014.81"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN, In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"32_CR11","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN, In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"32_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: Single Shot MultiBox Detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Redmon, J., et al.: You Only Look Once: unified, real-time object detection, In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger, In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517\u20136525 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"32_CR16","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement (2018)"},{"key":"32_CR17","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y. M.: YOLOv4: optimal speed and accuracy of object detection (2020)"},{"key":"32_CR18","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1109\/TITS.2015.2482461","volume":"17","author":"Y Yang","year":"2016","unstructured":"Yang, Y., Luo, H., Xu, H., Wu, F.: Towards real-time traffic sign detection and classification. IEEE Trans. Intell. Transp. Syst. 17, 2022\u20132031 (2016)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"32_CR19","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.eswa.2015.11.018","volume":"48","author":"SK Berkaya","year":"2016","unstructured":"Berkaya, S.K., Gunduz, H., Ozsen, O., Akinlar, C., Gunal, S.: On circular traffic sign detection and recognition. Expert Syst. Appl. 48, 67\u201375 (2016)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"32_CR20","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1109\/TITS.2012.2225618","volume":"13","author":"F Zaklouta","year":"2012","unstructured":"Zaklouta, F., Stanciulescu, B.: Real-Time traffic-sign recognition using tree classifiers. IEEE Trans. Intell. Transp. Syst. 13(4), 1507\u20131514 (2012)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"32_CR21","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1109\/TITS.2010.2054084","volume":"11","author":"H Gomez-Moreno","year":"2010","unstructured":"Gomez-Moreno, H., Maldonado-Bascon, S., Gil-Jimenez, P., Lafuente-Arroyo, S.: Goal evaluation of segmentation algorithms for traffic sign recognition. IEEE Trans. Intell. Transp. Syst. 11, 917\u2013930 (2010)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"32_CR22","unstructured":"Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition (2014)"},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"32_CR25","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1109\/TITS.2019.2911727","volume":"21","author":"U Kamal","year":"2020","unstructured":"Kamal, U., Tonmoy, T.I., Das, S., Hasan, M.K.: Automatic traffic sign detection and recognition using Segu-net and a modified Tversky loss function with l1-constraint. IEEE Trans. Intell. Transp. Syst. 21, 1467\u20131479 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"32_CR26","doi-asserted-by":"publisher","first-page":"117784","DOI":"10.1109\/ACCESS.2021.3106350","volume":"9","author":"Q Tang","year":"2021","unstructured":"Tang, Q., Cao, G., Jo, K.-H.: Integrated feature pyramid network with feature aggregation for traffic sign detection. IEEE Access 9, 117784\u2013117794 (2021)","journal-title":"IEEE Access"},{"issue":"4","key":"32_CR27","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1109\/TITS.2019.2913588","volume":"21","author":"D Tabernik","year":"2020","unstructured":"Tabernik, D., Sko\u010daj, D.: Deep learning for large-scale traffic-sign detection and recognition. IEEE Trans. Intell. Transp. Syst. 21(4), 1427\u20131440 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"32_CR28","doi-asserted-by":"publisher","first-page":"57120","DOI":"10.1109\/ACCESS.2019.2913882","volume":"7","author":"Z Liu","year":"2019","unstructured":"Liu, Z., Du, J., Tian, F., Wen, J.: MR-CNN: a multi-scale region-based convolutional neural network for small traffic sign recognition. IEEE Access 7, 57120\u201357128 (2019)","journal-title":"IEEE Access"},{"key":"32_CR29","doi-asserted-by":"publisher","first-page":"38931","DOI":"10.1109\/ACCESS.2020.2975828","volume":"8","author":"Y Jin","year":"2020","unstructured":"Jin, Y., Fu, Y., Wang, W., Guo, J., Ren, C., Xiang, X.: Multi-Feature fusion and enhancement single shot detector for traffic sign recognition. IEEE Access 8, 38931\u201338940 (2020)","journal-title":"IEEE Access"},{"key":"32_CR30","doi-asserted-by":"crossref","unstructured":"Luo, H.-W., Zhang, C.-S., Pan, F.-C., Ju, X.-M.: Contextual-YOLOV3: implement better small object detection based deep learning In: 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), pp. 134\u2013141 (2019)","DOI":"10.1109\/MLBDBI48998.2019.00032"},{"key":"32_CR31","doi-asserted-by":"publisher","first-page":"124963","DOI":"10.1109\/ACCESS.2021.3109798","volume":"9","author":"L Wang","year":"2021","unstructured":"Wang, L., Zhou, K., Chu, A., Wang, G., Wang, L.: An improved light-weight traffic sign recognition algorithm based on YOLOv4-tiny. IEEE Access 9, 124963\u2013124971 (2021)","journal-title":"IEEE Access"},{"key":"32_CR32","doi-asserted-by":"crossref","unstructured":"Wang, H., Yu, H.: Traffic sign detection algorithm based on improved YOLOv4, pp. 1946\u20131950 (2020)","DOI":"10.1109\/ITAIC49862.2020.9339181"},{"key":"32_CR33","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., Hu, S.: Traffic-Sign Detection and Classification in the Wild In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2110\u20132118 (2016)","DOI":"10.1109\/CVPR.2016.232"},{"key":"32_CR34","doi-asserted-by":"publisher","first-page":"83611","DOI":"10.1109\/ACCESS.2020.2991195","volume":"8","author":"L Wei","year":"2020","unstructured":"Wei, L., Xu, C., Li, S., Tu, X.: Traffic sign detection and recognition using novel center-point estimation and local features. IEEE Access 8, 83611\u201383621 (2020)","journal-title":"IEEE Access"},{"key":"#cr-split#-32_CR35.1","doi-asserted-by":"crossref","unstructured":"Xiao, D.,Liu, L.: Super-Resolution-Based traffic prohibitory sign recognition, In: 2019 IEEE 21st International Conference on High Performance Computing and Communications IEEE 17th International Conference on Smart City","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00332"},{"key":"#cr-split#-32_CR35.2","unstructured":"IEEE 5th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), pp. 2383-2388 (2019)"},{"key":"32_CR36","doi-asserted-by":"publisher","first-page":"171170","DOI":"10.1109\/ACCESS.2020.3024583","volume":"8","author":"L Liu","year":"2020","unstructured":"Liu, L., Wang, Y., Li, K., Li, J.: Focus First: Coarse-to-fine traffic sign detection with stepwise learning. IEEE Access 8, 171170\u2013171183 (2020)","journal-title":"IEEE Access"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31417-9_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T22:46:45Z","timestamp":1729378005000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31417-9_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031314162","9783031314179"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31417-9_32","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":"7 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nagpur","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":"4 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/vnit.ac.in\/cvip2022\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"110","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":"11","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":"36% - 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":"4","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)"}}]}}