{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:13:20Z","timestamp":1770394400104,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":15,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819557578","type":"print"},{"value":"9789819557585","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-5758-5_5","type":"book-chapter","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:59:32Z","timestamp":1770353972000},"page":"41-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Lightweight YOLOv11n Wind Turbine Blade Defect Detection Model Based on Dynamic Multi-scale Fusion and Task Alignment Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7454-0796","authenticated-orcid":false,"given":"He","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3521-0595","authenticated-orcid":false,"given":"Yihao","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Huijie","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2573-3062","authenticated-orcid":false,"given":"Zhumu","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qinglei","family":"Qi","sequence":"additional","affiliation":[]},{"given":"Xiaopu","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,7]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2024.115139","volume":"209","author":"AMG De","year":"2025","unstructured":"De, A.M.G., Oliveira, F.L.C., Ma\u00e7aira, P.M., et al.: Exploring complementary effects of solar and wind power generation. Renew. Sustain. Energy Rev. 209, 115139 (2025)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Verma, A.S., Yan, J.Q., Hu, W.F., Jiang, Z.Y., et al.: A review of impact loads on composite wind turbine blades: impact threats and classification, Renew. Sustain. Energy Rev. 178 (2023)","DOI":"10.1016\/j.rser.2023.113261"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"McKenna, R., Lilliestam, J., Heinrichs, H.U., et al.: System impacts of wind energy developments: key research challenges and opportunities. Joule 9(1) (2025)","DOI":"10.1016\/j.joule.2024.11.016"},{"key":"5_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2024.114991","volume":"207","author":"G Si","year":"2025","unstructured":"Si, G., Xia, T., Gebraeel, N., et al.: Holistic opportunistic maintenance scheduling and routing for offshore wind farms. Renew. Sustain. Energy Rev. 207, 114991 (2025)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Wang, Y.B., Peng, T., Wang, W.H., Luo, M.: High-efficient view planning for surface inspection based on parallel deep reinforcement learning, Adv. Eng. Inf. 55 (2023)","DOI":"10.1016\/j.aei.2022.101849"},{"issue":"2","key":"5_CR6","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1080\/10589759.2024.2324373","volume":"40","author":"X Zhu","year":"2025","unstructured":"Zhu, X., Guo, Z., Zhou, Q., et al.: Damage identification of wind turbine blades based on deep learning and ultrasonic testing. Nondestructive Test. Eval. 40(2), 508\u2013533 (2025)","journal-title":"Nondestructive Test. Eval."},{"issue":"1","key":"5_CR7","doi-asserted-by":"publisher","first-page":"24558","DOI":"10.1038\/s41598-024-74798-3","volume":"14","author":"Z Wu","year":"2024","unstructured":"Wu, Z., Zhang, Y., Wang, X., et al.: Algorithm for detecting surface defects in wind turbines based on a lightweight YOLO model. Sci. Rep. 14(1), 24558 (2024)","journal-title":"Sci. Rep."},{"key":"5_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.102292","volume":"59","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Zheng, Y., Shao, Z., et al.: Defect detection of the surface of wind turbine blades combining attention mechanism. Adv. Eng. Inform. 59, 102292 (2024)","journal-title":"Adv. Eng. Inform."},{"key":"5_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.113222","volume":"220","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Yang, Y., Sun, J., et al.: Surface defect detection of wind turbine based on lightweight YOLOv5s model. Measurement 220, 113222 (2023)","journal-title":"Measurement"},{"key":"5_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2025.112641","volume":"230","author":"W Zhao","year":"2025","unstructured":"Zhao, W., Li, W., Du, Y.: Wind turbine blade rotational condition monitoring based on RBs-YOLO deep learning model. Mech. Syst. Signal Process. 230, 112641 (2025)","journal-title":"Mech. Syst. Signal Process."},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Ma, Y.L., Jiang, X., Tang, Z., et al.: Wind turbine blade defect detection algorithm based on lightweight MES-YOLOv8n. IEEE Sens. J. (2024)","DOI":"10.1109\/JSEN.2024.3430351"},{"key":"5_CR12","unstructured":"Wei, H., Liu, X., Xu, S., et al.: DWRSeg: rethinking efficient acquisition of multi-scale contextual information for real-time semantic segmentation. arXiv preprint arXiv:2212.01173 (2022)"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, Y., Ge, Y., et al.: UniRepLKNet: a universal perception large-kernel ConvNet for audio video point cloud time-series and image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5513\u20135524(2024)","DOI":"10.1109\/CVPR52733.2024.00527"},{"key":"5_CR14","unstructured":"Ma, S., Xu, Y.: a loss for efficient and accurate bounding box regression. arXiv preprint arXiv:2307.07662 (2023)"},{"key":"5_CR15","unstructured":"Zhang, H., Xu, C., Zhang, S.: Inner-IOU: more effective intersection over union loss with auxiliary bounding box. arXiv preprint arXiv:2311.02877 (2023)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5758-5_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:59:34Z","timestamp":1770353974000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5758-5_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819557578","9789819557585"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5758-5_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"7 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}