{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T10:07:55Z","timestamp":1784282875889,"version":"3.55.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031500688","type":"print"},{"value":"9783031500695","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-50069-5_36","type":"book-chapter","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T06:02:34Z","timestamp":1705644154000},"page":"440-450","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Convolutional Neural Networks and Vision Transformers in Product GS1 GPC Brick Code Recognition"],"prefix":"10.1007","author":[{"given":"Maciej","family":"Szymkowski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maciej","family":"Niemir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beata","family":"Mrugalska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalid","family":"Saeed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Sean, L.: The Global Data Synchronisation Network (GDSN): tchnology and standards improving supply chain efficiency. In: First International Technology Management Conference, pp. 630\u2013637. IEEE (2011)","DOI":"10.1109\/ITMC.2011.5996036"},{"key":"36_CR2","unstructured":"O\u2019Shea, K., Nash, R.: An Introduction to Convolutional Neural Networks. arXiv: 1511.08458 [cs.NE] (2015)"},{"key":"36_CR3","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., et. al.: An Image is worth 16x16 words: Transformers for image recognition at scale. In: 2021 International Conference on Learning Representation, ICLR 2021, Proceedings"},{"key":"36_CR4","unstructured":"https:\/\/www.section.io\/engineering-education\/introduction-to-yolo-algorithm-for-object-detection\/. Accessed 29th May 2023"},{"key":"36_CR5","unstructured":"https:\/\/medium.com\/cord-tech\/yolov8-for-object-detection-explained-practical-example-23920f77f66a. Accessed 29th May 2023"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., et. al.: SSD: Single Shot MultiBox Detector. arXiv: 1512.02325 [cs.CV] (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., et. al.: Focal Loss for Dense Object Detection. arXiv: 1708.02002 [cs.CV] (2018)","DOI":"10.1109\/ICCV.2017.324"},{"key":"36_CR8","doi-asserted-by":"publisher","first-page":"2610","DOI":"10.1016\/j.procs.2020.04.283","volume":"171","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Srivastava, S.: Object detection system based on convolution neural networks using single shot multi-box detector. Procedia Comput. Sci. 171, 2610\u20132617 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., et. al.: Residual attention network for image classification. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21\u201326 July 2017, Honolulu, USA, Proceedings, pp. 3156\u20133164 (2017)","DOI":"10.1109\/CVPR.2017.683"},{"key":"36_CR10","volume":"2020","author":"H Hakim","year":"1804","unstructured":"Hakim, H., Fadhil, A.: Survey: convolution neural networks in object detection. J. Phys. Conf. Ser. 2020, 012095 (1804)","journal-title":"J. Phys. Conf. Ser."},{"key":"36_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3032981","author":"K Wang","year":"2020","unstructured":"Wang, K., Liu, M.: Object recognition at night scene based on DCGAN and faster R-CNN. IEEE Access (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3032981","journal-title":"IEEE Access"},{"issue":"3","key":"36_CR12","first-page":"49","volume":"10","author":"S Emmanuel","year":"2022","unstructured":"Emmanuel, S., Onuodu, F.E.: Object detection using convolutional neural network transfer learning. Int. J. Innov. Res. Eng. Multidiscipl. Phys. Sci. 10(3), 49\u201359 (2022)","journal-title":"Int. J. Innov. Res. Eng. Multidiscipl. Phys. Sci."},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Mezzadri Centeno, T., Silverio Lopes, H., Kleber Felisberto, M., et al.: Object detection for computer vision using a robust genetic algorithm. In: Rothlauf, F., et. al. (eds.) Applications of Evolutionary Computing\u201d, EvoWorkkshops 2005, Lausanne, Switzerland, March\/April 2005, Proceedings, pp. 284\u2013293 (2005)","DOI":"10.1007\/978-3-540-32003-6_29"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Wasala, M., Kryjak, T.: Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream. arXiv: 2204.10619 [cs.CV] (2022)","DOI":"10.36227\/techrxiv.19635429.v1"},{"key":"36_CR15","unstructured":"Prakash, C., Karam, L.: It GAN DO Better: GAN-based Detection of Objects on Images with Varying Quality. arXiv: 1912.01707 [cs.CV] (2019)"},{"key":"36_CR16","doi-asserted-by":"publisher","unstructured":"Niemir, M., Mrugalska, B.: Basic Product Data in E-Commerce: Specifications and Problems of Data Exchange. ERSJ, vol. XXIV, no. Special Issue 5, pp. 317\u2013329 (2021). https:\/\/doi.org\/10.35808\/ersj\/2735","DOI":"10.35808\/ersj\/2735"},{"key":"36_CR17","doi-asserted-by":"publisher","unstructured":"Niemir, M., Mrugalska, B.: Monitoring and improvement of data quality in product catalogs using defined normalizers and validation patterns. In: Human Factors in Engineering, pp. 173\u2013187. CRC Press (2023). https:\/\/doi.org\/10.1201\/9781003383444","DOI":"10.1201\/9781003383444"},{"key":"36_CR18","unstructured":"Muszy\u0144ski, K., Niemir, M., Skwarek, S.: Searching for Ai solutions to improve the quality of master data affecting consumer safety. In: Business Logistics in Modern Management, Osijek, Croatia: Faculty of Economics in Osijek, pp. 121\u2013140 (2022). http:\/\/blmm-conference.com\/wp-content\/uploads\/BLMM2022_Conference_Proceedings.pdf. Accessed 10 Jan 2023"},{"key":"36_CR19","doi-asserted-by":"publisher","unstructured":"Zhang, W., Jiang, D.: The marker-based watershed segmentation algorithm of ore image. In: 2011 IEEE 3rd International Conference on Communication Software and Networks (2011). https:\/\/doi.org\/10.1109\/ICCSN.2011.6014611","DOI":"10.1109\/ICCSN.2011.6014611"},{"key":"36_CR20","unstructured":"https:\/\/github.com\/danielgatis\/rembg. Accessed 10th May 2023"},{"key":"36_CR21","unstructured":"https:\/\/huggingface.co\/. Accessed 10th May 2023"},{"key":"36_CR22","unstructured":"Wightman, R., Touvron, H., Jegou, H.: \u201cResNet strikes back: An improved training procedure in timm\u201d, arXiv: 2110.00476 [cs.CV], 2021"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, J., Sheng, B., et. al.: Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Trans. Indust. Inform. 18(1), 163\u2013173 (2022)","DOI":"10.1109\/TII.2021.3085669"},{"key":"36_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02749-y","author":"J Shi","year":"2022","unstructured":"Shi, J., Li, T., Xu, J.: Recursive lightweight convolutional neural networks that make noisy images purer and purer. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-022-02749-y","journal-title":"Vis. Comput."},{"key":"36_CR25","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1007\/s00371-020-01901-w","volume":"37","author":"T Yang","year":"2021","unstructured":"Yang, T., Zhang, T., Huang, L.: Detection of defects in voltage-dependent resistors using stacked-block-based convolutional neural networks. Vis. Comput. 37, 1559\u20131567 (2021)","journal-title":"Vis. Comput."}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50069-5_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T14:59:13Z","timestamp":1731077953000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50069-5_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031500688","9783031500695"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50069-5_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"28 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"385","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":"149","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":"39% - 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":"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)"}}]}}