{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:01:30Z","timestamp":1743019290242,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442094"},{"type":"electronic","value":"9783031442100"}],"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\/978-3-031-44210-0_8","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T08:02:34Z","timestamp":1695283354000},"page":"90-101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Improved YOLOv5 with Structural Reparameterization for Surface Defect Detection"],"prefix":"10.1007","author":[{"given":"Yixuan","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liying","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Song, X., Bai, F., Wu, J., Chen, X., Zhang, T.: Wood knot defects recognition with gray-scale histogram features. Laser Optoelectron. Prog. 52, 20503904 (2015)","DOI":"10.3788\/LOP52.031501"},{"issue":"389","key":"8_CR2","first-page":"397","volume":"42","author":"JP Yun","year":"2009","unstructured":"Yun, J.P., Choi, S.H., Kim, J.W., Kim, S.W.: Automatic detection of cracks in raw steel block using Gabor filter optimized by univariate dynamic encoding algorithm for searches (uDEAS). NDT E Int. 42(389), 397 (2009)","journal-title":"NDT E Int."},{"issue":"428","key":"8_CR3","first-page":"200","volume":"196","author":"H Ren","year":"2019","unstructured":"Ren, H., Tian, K., Hong, S., Dong, B., Xing, F., Qin, L.: Visualized investigation of defect in cementitious materials with electrical resistance tomography. Constr. Build. Mater. 196(428), 200 (2019)","journal-title":"Constr. Build. Mater."},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.jmsy.2019.03.002","volume":"51","author":"J Wang","year":"2019","unstructured":"Wang, J., Fu, P., Gao, R.X.: Machine vision intelligence for product defect inspection based on deep learning and Hough transform. J. Manuf. Syst. 51, 52\u201360 (2019)","journal-title":"J. Manuf. Syst."},{"issue":"6","key":"8_CR5","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., et al.: 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":"8_CR6","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Comput. Soc. (2014)","DOI":"10.1109\/CVPR.2014.81"},{"issue":"9","key":"8_CR7","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. Comput. Sci. (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Ren, Q., Geng, J., Li, J.: Slighter Faster R-CNN for real-time detection of steel strip surface defects. In: 2018 Chinese AutomationCongress (CAC) 2018","DOI":"10.1109\/CAC.2018.8623407"},{"issue":"1","key":"8_CR10","doi-asserted-by":"publisher","first-page":"012073","DOI":"10.1088\/1742-6596\/2246\/1\/012073","volume":"2246","author":"B Si","year":"2022","unstructured":"Si, B., Yasengjiang, M., Huawen, W.: Deep learning-based defect detection for hot-rolled strip steel. J. Phys. Conf. Series 2246(1), 012073 (2022)","journal-title":"J. Phys. Conf. Series"},{"key":"8_CR11","doi-asserted-by":"publisher","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., et al.: Feature pyramid networks for object detection. IEEE Comput. Soc. (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: Computer Vision & Pattern Recognition. IEEE (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: IEEE Conference on Computer Vision & Pattern Recognition. IEEE, pp. 6517\u20136525 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"8_CR14","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. arXiv e-prints (2018)"},{"key":"8_CR15","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.: YOLOv4: Optimal Speed and Accuracy of Object Detection (2020)"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., et al.: TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios (2021)","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., et al.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"8_CR18","doi-asserted-by":"publisher","unstructured":"Wang, C.Y., Liao, H.Y.M., Wu, Y.H., et al.: CSPNet: a new backbone that can enhance learning capability of CNN. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE (2020).https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00203","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Li, H., Xiong, P., An, J., et al.: Pyramid Attention Network for Semantic Segmentation (2018). https:\/\/doi.org\/10.48550\/arXiv.1805.10180","DOI":"10.48550\/arXiv.1805.10180"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., et al.: RepVGG: making VGG-style convnets great again (2021)","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate Attention for Efficient Mobile Network Design (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"issue":"4","key":"8_CR22","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","volume":"69","author":"Y He","year":"2020","unstructured":"He, Y., Song, K., Meng, Q., Yan, Y.: An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans. Instrum. Meas. 69(4), 1493\u20131504 (2020). https:\/\/doi.org\/10.1109\/TIM.2019.2915404","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"8_CR23","unstructured":"Hu, J., Shen, L., et al.: Squeeze-and-Excitation Networks. IEEE transactions on pattern analysis and machine intelligence (2019)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44210-0_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T08:03:49Z","timestamp":1695283429000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44210-0_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442094","9783031442100"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44210-0_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted  : 9 Abstract","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)"}}]}}