Correction: A cable insulation defect classification method based on CNN-transformer

Correction: A cable insulation defect classification method based on CNN-transformerName of all authors as they appear in the published original article (INSERT ONLY IF correcting author names or adding authors. Insert the correct version of the author list)Affiliations of all authors as they appear in the published original version of the article (INSERT ONLY IF correcting affiliation(s) or adding affiliation(s). Insert the correct version of the affiliation(s))* Correspondence: [email protected]: cable, insulation defect, ultrasonic reflection, defect recognition, CNN-transformerCorrection on: Zhao N, Duan Z, Li Q, et al. A cable insulation defect classification method based on CNN-transformer[J]. Frontiers in Physics, 2024, 12: 1432527.Incorrect funding Grant number incorrectAn incorrect number was provided for State Grid Hebei Electric Power Co., Ltd.. The correct number is KJ2022-006.The original version of this article has been updated.Conflict of interest statementThe conflict of interest statement was erroneously given as Authors NZ, ZD, QL, KG, ZZ, and BL were employed by State Grid Shijiazhuang Electric Power Supply Company. The authors declare that this study received funding from State Grid Hebei Electric Power Co., Ltd. (No. KJ2022-016). The funder was involved in the study design, collection, analysis, interpretation of data, the writing of this article, and the decision to submit it for publication. The correct conflict of interest statement is Authors NZ, ZD, QL, KG, ZZ, and BL were employed by State Grid Shijiazhuang Electric Power Supply Company. The authors declare that this study received funding from State Grid Hebei Electric Power Co., Ltd. (No. KJ2022-006). The funder was involved in the study design, collection, analysis, interpretation of data, the writing of this article, and the decision to submit it for publication.The original version of this article has been updated.

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Publication Details

Journal
Frontiers in Physics
Published
2025-10-15
DOI
https://doi.org/10.3389/fphy.2025.1653145
Citations
1
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
1.24
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article

Correction: A cable insulation defect classification method based on CNN-transformer

Ziguang Zhang, Qian Li, Kang Guo, Ning Zhao et al.
1 citations
Frontiers in Physics
Industrial Vision Systems and Defect Detection
1.24
article

Correction: A cable insulation defect classification method based on CNN-transformer

Ziguang Zhang, Qian Li, Kang Guo, Ning Zhao, Baoan Liu, Zhiguo Duan
article en
1 citations

Abstract

Correction: A cable insulation defect classification method based on CNN-transformerName of all authors as they appear in the published original article (INSERT ONLY IF correcting author names or adding authors. Insert the correct version of the author list)Affiliations of all authors as they appear in the published original version of the article (INSERT ONLY IF correcting affiliation(s) or adding affiliation(s). Insert the correct version of the affiliation(s))* Correspondence: [email protected]: cable, insulation defect, ultrasonic reflection, defect recognition, CNN-transformerCorrection on: Zhao N, Duan Z, Li Q, et al. A cable insulation defect classification method based on CNN-transformer[J]. Frontiers in Physics, 2024, 12: 1432527.Incorrect funding Grant number incorrectAn incorrect number was provided for State Grid Hebei Electric Power Co., Ltd.. The correct number is KJ2022-006.The original version of this article has been updated.Conflict of interest statementThe conflict of interest statement was erroneously given as Authors NZ, ZD, QL, KG, ZZ, and BL were employed by State Grid Shijiazhuang Electric Power Supply Company. The authors declare that this study received funding from State Grid Hebei Electric Power Co., Ltd. (No. KJ2022-016). The funder was involved in the study design, collection, analysis, interpretation of data, the writing of this article, and the decision to submit it for publication. The correct conflict of interest statement is Authors NZ, ZD, QL, KG, ZZ, and BL were employed by State Grid Shijiazhuang Electric Power Supply Company. The authors declare that this study received funding from State Grid Hebei Electric Power Co., Ltd. (No. KJ2022-006). The funder was involved in the study design, collection, analysis, interpretation of data, the writing of this article, and the decision to submit it for publication.The original version of this article has been updated.

Frontiers in PhysicsVol. 13
Openalex Percentile: Top 14%
Industrial Vision Systems and Defect Detection
1.24
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