{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T21:42:26Z","timestamp":1779313346489,"version":"3.51.4"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031324420","type":"print"},{"value":"9783031324437","type":"electronic"}],"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\/978-3-031-32443-7_9","type":"book-chapter","created":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T20:30:19Z","timestamp":1685219419000},"page":"127-137","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Surface Defect Detection Algorithm of Aluminum Sheet Based on Improved Yolov3"],"prefix":"10.1007","author":[{"given":"Liu","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoxiong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,28]]},"reference":[{"issue":"2","key":"9_CR1","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jmst.2018.09.004","volume":"35","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Song, B., Wei, Q., et al.: A review of selective laser melting of aluminum alloys: processing, microstructure, property and developing trends. J. Mater. Sci. Technol. 35(2), 270\u2013284 (2019)","journal-title":"J. Mater. Sci. Technol."},{"key":"9_CR2","first-page":"1","volume":"1575","author":"X Tao","year":"2018","unstructured":"Tao, X., Zhang, D., Ma, W., et al.: Automatic metallic surface defect detection and recognition with convolutional neural networks. Appl. Sci. 1575, 1\u201315 (2018)","journal-title":"Appl. Sci."},{"issue":"2","key":"9_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10921-017-0396-6","volume":"36","author":"W Dehui","year":"2017","unstructured":"Dehui, W., Lingxin, S., Wang, X., et al.: A novel non-destructive testing method by measuring the change rate of magnetic flux leakage. J. Nondestr. Eval. 36(2), 1\u201311 (2017)","journal-title":"J. Nondestr. Eval."},{"key":"9_CR4","doi-asserted-by":"publisher","first-page":"166342","DOI":"10.1016\/j.ijleo.2021.166342","volume":"231","author":"M Malarvel","year":"2021","unstructured":"Malarvel, M., Singh, H.: An autonomous technique for weld defects detection and classification using multi-class support vector machine in X-radiography image. Optik 231, 166342 (2021)","journal-title":"Optik"},{"key":"9_CR5","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1007\/978-3-030-17795-9_10","volume-title":"Advances in Computer Vision","author":"N O\u2019Mahony","year":"2020","unstructured":"O\u2019Mahony, N., et al.: Deep learning vs. traditional computer vision. In: Arai, K., Kapoor, S. (eds.) CVC 2019. AISC, vol. 943, pp. 128\u2013144. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-17795-9_10"},{"issue":"24","key":"9_CR6","doi-asserted-by":"publisher","first-page":"5755","DOI":"10.3390\/ma13245755","volume":"13","author":"J Yang","year":"2020","unstructured":"Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., Tang, S.: Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24), 5755 (2020)","journal-title":"Materials"},{"issue":"6","key":"9_CR7","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."},{"issue":"2","key":"9_CR8","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1049\/trit.2019.0019","volume":"4","author":"R Ding","year":"2019","unstructured":"Ding, R., Dai, L., Li, G., et al.: TDD-net: a tiny defect detection network for printed circuit boards. CAAI Trans. Intell. Technol. 4(2), 110\u2013116 (2019)","journal-title":"CAAI Trans. Intell. Technol."},{"issue":"1_suppl","key":"9_CR9","doi-asserted-by":"publisher","first-page":"82","DOI":"10.14504\/ajr.8.S1.11","volume":"8","author":"D He","year":"2021","unstructured":"He, D., Wen, J., Lai, Z., et al.: Textile fabric defect detection based on improved faster R-CNN. AATCC J. Res\/ 8(1_suppl), 82\u201390 (2021)","journal-title":"AATCC J. Res."},{"issue":"9","key":"9_CR10","doi-asserted-by":"publisher","first-page":"3467","DOI":"10.3390\/s22093467","volume":"22","author":"Z Guo","year":"2022","unstructured":"Guo, Z., Wang, C., Yang, G., et al.: MSFT-YOLO: improved YOLOv5 based on transformer for detecting defects of steel surface. Sensors 22(9), 3467 (2022)","journal-title":"Sensors"},{"key":"9_CR11","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.neucom.2021.03.094","volume":"448","author":"Z Yao","year":"2021","unstructured":"Yao, Z., Wang, L.: ERBANet: enhancing region and boundary awareness for salient object detection. Neurocomputing 448, 152\u2013167 (2021)","journal-title":"Neurocomputing"},{"key":"9_CR12","doi-asserted-by":"publisher","first-page":"3092","DOI":"10.1109\/TIP.2019.2957850","volume":"29","author":"Y-L Li","year":"2020","unstructured":"Li, Y.-L., Wang, S.: HAR-Net: Joint learning of hybrid attention for single-stage object detection. IEEE Transactions on Image Processing 29, 3092\u20133103 (2020)","journal-title":"IEEE Transactions on Image Processing"},{"issue":"10","key":"9_CR13","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"9_CR14","unstructured":"Hu, R., Liu, Y., Gu, K., Min, X., Zhai, G.: Toward a no-reference quality metric for camera-captured images. IEEE Trans. Cybern. (2021)"},{"key":"9_CR15","doi-asserted-by":"publisher","first-page":"102045","DOI":"10.1016\/j.displa.2021.102045","volume":"69","author":"R Hu","year":"2021","unstructured":"Hu, R., Liu, Y., Wang, Z., et al.: Blind quality assessment of night-time image. Displays 69, 102045 (2021)","journal-title":"Displays"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Mobile Networks and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-32443-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T20:34:39Z","timestamp":1685219679000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-32443-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031324420","9783031324437"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-32443-7_9","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"28 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MONAMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Mobile Networks and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"monami2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mon-ami.eai-conferences.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","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":"Confyplus.eai.eu","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78","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":"31","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":"40% - 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":"1","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)"}}]}}