{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T13:35:45Z","timestamp":1786541745507,"version":"build-2736575974"},"reference-count":48,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52378300"],"award-info":[{"award-number":["52378300"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["52378300"],"award-info":[{"award-number":["52378300"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Key Scientific Research Projects of Colleges and Universities in Henan province","award":["26A560013"],"award-info":[{"award-number":["26A560013"]}]},{"name":"Key R&D and Promotion Projects in Henan Province","award":["262102320052"],"award-info":[{"award-number":["262102320052"]}]},{"name":"Henan Outstanding Youth Science Foundation","award":["242300421014"],"award-info":[{"award-number":["242300421014"]}]},{"name":"China Construction Sixth Engineering Bureau Corp., Ltd.","award":["CSCEC6B-2024-Z-4"],"award-info":[{"award-number":["CSCEC6B-2024-Z-4"]}]},{"award":["CSCEC6B-2024-Z-4"],"award-info":[{"award-number":["CSCEC6B-2024-Z-4"]}],"id":[{"id":"https:\/\/ror.org\/050zcgq96","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate detection of surface defects in wind turbine blades is critical for condition monitoring and preventive maintenance of wind energy systems. Defects such as cracks, burns, deformation, and peeling are characterized by small dimensions, irregular morphologies, and low contrast, limiting the effectiveness of conventional feature extraction methods. Although YOLOv13 enhances high-order feature correlation and information flow, its fixed-grid spatial sampling and content-agnostic upsampling operations remain limited in adapting to irregular defect geometries and preserving fine-grained boundary information. This study proposes YOLOv13-ADR, an enhanced detection framework integrating Adaptive Deformable Convolution (ADConv) and a Nearest Neighbor Content Perception Recombination (NNCPR) module. ADConv applies a geometry-driven kernel permutation strategy to strengthen multi-scale feature representation, while NNCPR improves neighborhood-aware perception for modeling geometric deformations. A Focus-IoU loss function incorporating an anchor-quality perception mechanism is introduced to accelerate training convergence and improve bounding box regression precision. Additional optimizations include modifications to the DS-C3k2 module and upsampling strategy. Experiments on a wind turbine blade defect dataset demonstrate that YOLOv13-ADR achieves a 7.26-percentage-point improvement in mean average precision over YOLOv8n, with enhanced small-defect recognition and reduced localization errors, demonstrating improved detection precision and localization performance relevant to early fault detection and structural health monitoring of wind turbine blades.<\/jats:p>","DOI":"10.3390\/s26165111","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T12:53:48Z","timestamp":1786539228000},"page":"5111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLOv13-ADR: An Adaptive Deformable Convolution and Neighborhood-Aware Recombination Network for Wind Turbine Blade Defect Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2523-8432","authenticated-orcid":false,"given":"Xinwei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2317-5324","authenticated-orcid":false,"given":"Muhammad Moman","family":"Shahzad","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National University of Sciences and Technology, Balochistan Campus, Quetta 86000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0652-1849","authenticated-orcid":false,"given":"Shixuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Kang-Ju Construction Parts Certification Center, Centre of Science and Technology Industrial Development, Ministry of Housing and Urban-Rural Development of the People\u2019s Republic of China, Beijing 100835, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3509-9480","authenticated-orcid":false,"given":"Tianlong","family":"Wang","sequence":"additional","affiliation":[{"name":"CSCEC City Construction Development Co., Ltd., Beijing 100037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8134-9797","authenticated-orcid":false,"given":"Zhihao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122690","DOI":"10.1016\/j.oceaneng.2025.122690","article-title":"Parametric sensitivity analysis of soil properties for retrofitted offshore wind turbine jacket substructures under combined environmental loading","volume":"341","author":"Shahzad","year":"2025","journal-title":"Ocean Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104862","DOI":"10.1016\/j.apor.2025.104862","article-title":"Analyzing the structural integrity of retrofitted jacket substructures for an offshore wind turbine under nonlinear dynamic loading","volume":"165","author":"Shahzad","year":"2025","journal-title":"Appl. Ocean Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"119969","DOI":"10.1016\/j.renene.2024.119969","article-title":"Wind turbine blade icing risk assessment considering power output predictions based on SCSO-IFCM clustering algorithm","volume":"223","author":"Wang","year":"2024","journal-title":"Renew. Energy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"115428","DOI":"10.1016\/j.measurement.2024.115428","article-title":"Anomaly identification of wind turbine blades based on Mel-Spectrogram Difference feature of aerodynamic noise","volume":"240","author":"Li","year":"2025","journal-title":"Measurement"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111364","DOI":"10.1016\/j.asoc.2024.111364","article-title":"A wind turbine damage detection algorithm designed based on YOLOv8","volume":"154","author":"Liu","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113414","DOI":"10.1016\/j.oceaneng.2022.113322","article-title":"Operation and maintenance optimization of offshore wind farms based on digital twin: A review","volume":"268","author":"Xia","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110886","DOI":"10.1016\/j.rser.2021.110886","article-title":"Offshore wind turbine operations and maintenance: A state-of-the-art review","volume":"144","author":"Ren","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"126746","DOI":"10.1016\/j.oceaneng.2026.126746","article-title":"Seismic demand of jacket and monopile offshore wind turbine support structures under combined loading: Effect of ground motion significant duration","volume":"363","author":"Shahzad","year":"2026","journal-title":"Ocean Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"115009","DOI":"10.1016\/j.oceaneng.2023.115009","article-title":"A digital twin-based framework for simulation and monitoring analysis of floating wind turbine structures","volume":"283","author":"Liu","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1007\/s40722-024-00335-z","article-title":"Predictive maintenance for offshore wind turbines through deep learning and online clustering of unsupervised subsystems: A real-world implementation","volume":"10","author":"Beji","year":"2024","journal-title":"J. Ocean Eng. Mar. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.measurement.2018.09.011","article-title":"Deep features based on a DCNN model for classifying imbalanced weld flaw types","volume":"131","author":"Hou","year":"2019","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3076","DOI":"10.1109\/TAES.2025.3644834","article-title":"Graph convolutional contrastive learning for few-shot open-set fault diagnosis in electro-hydrostatic actuators","volume":"62","author":"Jia","year":"2026","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"15585","DOI":"10.1109\/TAES.2025.3589534","article-title":"Lightweight fault diagnosis via Siamese network for few-shot EHA circuit analysis","volume":"61","author":"Jia","year":"2025","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201323). Cascade R-CNN: Delving into high quality object detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder\u2013decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s41074-019-0056-0","article-title":"Unsupervised anomaly detection with compact deep features for wind turbine blade images taken by a drone","volume":"11","author":"Wang","year":"2019","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_18","first-page":"3514212","article-title":"The multiclass fault diagnosis of wind turbine bearing based on multisource signal fusion and deep learning generative model","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6137","DOI":"10.1109\/TII.2025.3558342","article-title":"Large-scale floating offshore wind turbine anomaly detection and diagnosis system based on spatio-temporal transformation features combining Informer and XGBoost","volume":"21","author":"Yang","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/TIA.2024.3481190","article-title":"Wind turbine blade defect detection based on the genetic algorithm-enhanced YOLOv5 algorithm using synthetic data","volume":"61","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"28409","DOI":"10.1109\/JSEN.2024.3430351","article-title":"Wind turbine blade defect detection algorithm based on lightweight MES-YOLOv8n","volume":"24","author":"Ma","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"26065","DOI":"10.1109\/JSEN.2025.3567500","article-title":"MDFANet: Multidomain feature alignment surface defect detection for aircraft engine turbine blades","volume":"25","author":"Wen","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5033413","DOI":"10.1109\/TIM.2025.3571169","article-title":"Structure and depth-based blade detection algorithm for multistage aeroengine fan blade detection","volume":"74","author":"Suo","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106445","DOI":"10.1016\/j.ymssp.2019.106445","article-title":"Damage detection techniques for wind turbine blades: A review","volume":"141","author":"Du","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113222","DOI":"10.1016\/j.measurement.2023.113222","article-title":"Surface defect detection of wind turbine based on lightweight YOLOv5s model","volume":"220","author":"Zhang","year":"2023","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112386","DOI":"10.1016\/j.ymssp.2025.112386","article-title":"Large-scale wind turbine blade operational condition monitoring based on UAV and improved YOLOv5 deep learning model","volume":"226","author":"Li","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112641","DOI":"10.1016\/j.ymssp.2025.112641","article-title":"Wind turbine blade rotational condition monitoring based on RBs-YOLO deep learning model","volume":"230","author":"Zhao","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_28","first-page":"129","article-title":"Generation of wind turbine blade surface defect dataset based on StyleGAN3 and PBGMs","volume":"34","author":"Li","year":"2024","journal-title":"Smart Struct. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"120152","DOI":"10.1016\/j.renene.2024.120152","article-title":"Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation","volume":"224","author":"Hang","year":"2024","journal-title":"Renew. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"114443","DOI":"10.1016\/j.measurement.2024.114443","article-title":"Road surface crack detection method based on improved YOLOv5 and vehicle-mounted images","volume":"229","author":"Hu","year":"2024","journal-title":"Measurement"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"117410","DOI":"10.1016\/j.measurement.2025.117410","article-title":"A insulator defect detection network based on improved YOLOv7 for UAV aerial images","volume":"253","author":"You","year":"2025","journal-title":"Measurement"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103041","DOI":"10.1016\/j.aei.2024.103041","article-title":"A novel fault diagnosis method based on nonlinear-CWT and improved YOLOv8 for axial piston pump using output pressure signal","volume":"64","author":"Xia","year":"2025","journal-title":"Adv. Eng. Informat."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106265","DOI":"10.1016\/j.conengprac.2025.106265","article-title":"Adversarial domain adaptation with norm constraints for enhanced fault diagnosis in pumping units via surface motor power","volume":"157","author":"Zuo","year":"2025","journal-title":"Control Eng. Pract."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105656","DOI":"10.1016\/j.engappai.2022.105656","article-title":"Unsupervised fault diagnosis of wind turbine bearing via a deep residual deformable convolution network based on subdomain adaptation under time-varying speeds","volume":"118","author":"Liang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115300","DOI":"10.1016\/j.measurement.2024.115300","article-title":"ASD-YOLO: An aircraft surface defects detection method using deformable convolution and attention mechanism","volume":"238","author":"Huang","year":"2024","journal-title":"Measurement"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"463","DOI":"10.3233\/IDA-205143","article-title":"Automatic image detection of multi-type surface defects on wind turbine blades based on cascade deep learning network","volume":"25","author":"Mao","year":"2021","journal-title":"Intell. Data Anal."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"122299","DOI":"10.1016\/j.eswa.2023.122299","article-title":"RA2DC-Net: A residual augment-convolutions and adaptive deformable convolution for points-based anchor-free orientation detection network in remote sensing images","volume":"238","author":"Gao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mao, M., and Hong, M. (2025). YOLO object detection for real-time fabric defect inspection in the textile industry: A review of YOLOv1 to YOLOv11. Sensors, 25.","DOI":"10.3390\/s25072270"},{"key":"ref_39","first-page":"9700111","article-title":"SFC-YOLOv8: Enhanced strip steel surface defect detection using spatial-frequency domain-optimized YOLOv8","volume":"74","author":"Liu","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"83535","DOI":"10.1007\/s11042-024-18872-y","article-title":"YOLO-based object detection models: A review and its applications","volume":"83","author":"Vijayakumar","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/s10462-025-11253-3","article-title":"YOLO advances to its genesis: A decadal and comprehensive review of the You Only Look Once (YOLO) series","volume":"58","author":"Sapkota","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_42","unstructured":"Lei, M., Li, S., Wu, Y., Hu, H., Zhou, Y., Zheng, X., Ding, G., Du, S., Wu, Z., and Gao, Y. (2025). YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9725","DOI":"10.1109\/TII.2024.3383513","article-title":"MFANet: Multifeature Aggregation Network for Cross-Granularity Few-Shot Seamless Steel Tubes Surface Defect Segmentation","volume":"20","author":"Song","year":"2024","journal-title":"IEEE Trans. Ind. Informat."},{"key":"ref_44","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2026, February 01). Ultralytics YOLOv8, Version 8.0.0. GitHub Repository. Available online: https:\/\/github.com\/ultralytics\/ultralytics\/blob\/main\/docs\/en\/models\/yolov8.md."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","article-title":"End-to-end object detection with Transformers","volume":"12346","author":"Carion","year":"2020","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"107984","DOI":"10.52202\/079017-3429","article-title":"YOLOv10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_47","first-page":"78433","article-title":"YOLOv12: Attention-centric real-time object detectors","volume":"38","author":"Tian","year":"2025","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"185689","DOI":"10.1109\/ACCESS.2025.3625998","article-title":"Site Landscape Design Optimization Based on YOLOv13 and Multi-Task Bayesian Federated Learning (YOLOv13-HyperACE-BFL)","volume":"13","author":"Lv","year":"2025","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5111\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T12:57:06Z","timestamp":1786539426000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,12]]},"references-count":48,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26165111"],"URL":"https:\/\/doi.org\/10.3390\/s26165111","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,12]]}}}