{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:22:43Z","timestamp":1743106963719,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819755967"},{"type":"electronic","value":"9789819755974"}],"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\/978-981-97-5597-4_40","type":"book-chapter","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T09:10:06Z","timestamp":1722503406000},"page":"473-483","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A YOLOv7-Based Defect Detection Method for Metal Surfaces"],"prefix":"10.1007","author":[{"given":"Zhiwei","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyuan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuliang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"key":"40_CR1","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.procir.2019.02.123","volume":"79","author":"B Staar","year":"2019","unstructured":"Staar, B., L\u00fctjen, M., Freitag, M.: Anomaly detection with convolutional neural networks for industrial surface inspection. Procedia CIRP 79, 484\u2013489 (2019)","journal-title":"Procedia CIRP"},{"issue":"16","key":"40_CR2","doi-asserted-by":"publisher","first-page":"3556","DOI":"10.3390\/s19163556","volume":"19","author":"H Perez","year":"2019","unstructured":"Perez, H., Tah, J.H.M., Mosavi, A.: Deep learning for detecting building defects using convolutional neural networks. Sensors 19(16), 3556 (2019)","journal-title":"Sensors"},{"key":"40_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3087826","volume":"70","author":"DM Tsai","year":"2021","unstructured":"Tsai, D.M., Fan, S.K.S., Chou, Y.H.: Auto-annotated deep segmentation for surface defect detection. IEEE Trans. Instrum. Meas. 70, 1\u201310 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"40_CR4","volume":"41","author":"E Westphal","year":"2021","unstructured":"Westphal, E., Seitz, H.: A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks. Addit. Manuf. 41, 101965 (2021)","journal-title":"Addit. Manuf."},{"issue":"24","key":"40_CR5","first-page":"360","volume":"58","author":"L Sun","year":"2021","unstructured":"Sun, L., Wei, J., Zhu, D., et al.: Surface defect detection algorithm of aluminum profile based on AM-YOLOv3 mode. Laser Optoelectron. Prog. 58(24), 360\u2013370 (2021)","journal-title":"Laser Optoelectron. Prog."},{"issue":"24","key":"40_CR6","doi-asserted-by":"publisher","first-page":"32","DOI":"10.3901\/JME.2022.24.032","volume":"58","author":"K Li","year":"2022","unstructured":"Li, K., Qi, Y., Lei, S., et al.: Visual inspection of steel surface defects based on improved auxiliary classification generation adversarial network. J. Mech. Eng. 58(24), 32\u201340 (2022)","journal-title":"J. Mech. Eng."},{"key":"40_CR7","doi-asserted-by":"publisher","first-page":"66","DOI":"10.13462\/j.cnki.mmtamt.2022.11.015","volume":"11","author":"C Yang","year":"2022","unstructured":"Yang, C., Li, H., Pan, G.: Surface defect detection of aluminum profiles based on improved YOLOv4 algorithm. Modul. Mach. Tool Autom. Manuf. Tech. 11, 66\u201369 (2022). https:\/\/doi.org\/10.13462\/j.cnki.mmtamt.2022.11.015","journal-title":"Modul. Mach. Tool Autom. Manuf. Tech."},{"issue":"11","key":"40_CR8","doi-asserted-by":"publisher","first-page":"8389","DOI":"10.1007\/s00521-022-08112-5","volume":"35","author":"K Demir","year":"2023","unstructured":"Demir, K., Ay, M., Cavas, M., et al.: Automated steel surface defect detection and classification using a new deep learning-based approach. Neural Comput. Appl. 35(11), 8389\u20138406 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"11","key":"40_CR9","first-page":"2673","volume":"53","author":"J Nie","year":"2023","unstructured":"Nie, J., Jin, X., Yin, A., et al.: Detection and evaluation of coating defects in offshore booster stations based on improved faster-RCNN. Radio Eng. 53(11), 2673\u20132680 (2023)","journal-title":"Radio Eng."},{"key":"40_CR10","unstructured":"Wang, A., Yuan, J., Zhu, Y., et al.: Drum roller surface defect detection algorithm based on improved YOLOv8s. J. Zhejiang Univ. (Eng. Sci.) 58(02), 370\u2013380+387 (2024)"},{"key":"40_CR11","unstructured":"Zhou, J., Wang, J.: Review of YOLO object detection algorithms. J. Changzhou Inst. Technol. 36(01), 18\u201323+88 (2023)"},{"key":"40_CR12","unstructured":"Li, C., Li, L., Jiang, H., et al.: YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976 (2022)"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., Liao, H.Y.M.: YOLOv9: learning what you want to learn using programmable gradient information. arXiv preprint arXiv:2402.13616 (2024)","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"40_CR15","unstructured":"Li, C., Zhou, A., Yao, A.: Omni-dimensional dynamic convolution. arXiv preprint arXiv:2209.07947 (2022)"},{"key":"40_CR16","unstructured":"Siliang, M., Yong, X.: MPDIoU: a loss for efficient and accurate bounding box regression. arXiv preprint arXiv:2307.07662 (2023)"},{"key":"40_CR17","doi-asserted-by":"publisher","unstructured":"Li, W., Yao, X., Zhang, P., et al.: Research on improved YOLO-V7 steel surface defect detection algorithm. Mech. Sci. Technol. Aerosp. Eng., 1\u201310 (2024). https:\/\/doi.org\/10.13433\/j.cnki.1003-8728.20230368","DOI":"10.13433\/j.cnki.1003-8728.20230368"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5597-4_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T13:15:35Z","timestamp":1732540535000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5597-4_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755967","9789819755974"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5597-4_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}