{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:59:04Z","timestamp":1784645944855,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of State Grid Shanxi Electric Power Company","award":["52053023001N"],"award-info":[{"award-number":["52053023001N"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Aiming at the difficulties of scarcity of defect samples on the surface of power insulators, irregular morphology and insufficient pixel-level localization accuracy, this paper proposes a defect detection method based on graph feature diffusion distillation named GFDD. The feature bias problem is alleviated by constructing a dual-division teachers architecture with graph feature consistency constraints, while the cross-layer feature fusion module is utilized to dynamically aggregate multi-scale information to reduce redundancy; the diffusion distillation mechanism is designed to break through the traditional single-layer feature transfer limitation, and the global context modeling capability is enhanced by fusing deep semantics and shallow details through channel attention. In the self-built dataset, GFDD achieves 96.6% Pi.AUROC, 97.7% Im.AUROC and 95.1% F1-score, which is 2.4\u20133.2% higher than the existing optimal methods; it maintains excellent generalization and robustness in multiple public dataset tests. The method provides a high-precision solution for automated inspection of insulator surface defect and has certain engineering value.<\/jats:p>","DOI":"10.3390\/jimaging11060190","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T05:11:49Z","timestamp":1749532309000},"page":"190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Insulator Surface Defect Detection Method Based on Graph Feature Diffusion Distillation"],"prefix":"10.3390","volume":"11","author":[{"given":"Shucai","family":"Li","sequence":"first","affiliation":[{"name":"State Grid Shanxi Electric Power Company Lvliang Power Supply Company, Lvliang 033000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Yang","sequence":"additional","affiliation":[{"name":"State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yannong","family":"Hou","sequence":"additional","affiliation":[{"name":"State Grid Shanxi Electric Power Company Ultra High Voltage Substation Branch, Taiyuan 030021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingzhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software, Taiyuan University of Technology, Taiyuan 030001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109688","DOI":"10.1016\/j.epsr.2023.109688","article-title":"Summary of insulator defect detection based on deep learning","volume":"224","author":"Liu","year":"2023","journal-title":"Electr. 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