Transmission Line Insulator Defect Detection Method Based on YOLO-MLSL Model.
Saved in:
| Title: | Transmission Line Insulator Defect Detection Method Based on YOLO-MLSL Model. |
|---|---|
| Authors: | Zheng, Renhao1 (AUTHOR) 17775029211@163.com, Duan, Guoyong1 (AUTHOR), Cao, Xin1 (AUTHOR), Wang, Haofeng1 (AUTHOR) |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2305. 21p. |
| Subject Terms: | *Object recognition (Computer vision), *Loss functions (Statistics) |
| Abstract: | To address the challenges of insufficient small target recognition, difficulty in edge information extraction, and high computational overhead in insulator defect detection, this paper proposes a lightweight detection method based on the YOLO-MLSL model for transmission line insulator defect detection. First, a C2f-LRFCA module is introduced, effectively enhancing feature interaction through a long-range convolutional attention mechanism, thereby improving the perception of fine-grained defects. Second, an MEUM multi-scale feature enhancement module is designed to achieve more efficient contextual information fusion during upsampling, improving the detection performance for multi-scale targets. Third, the ShapeIoU loss function is employed to improve the bounding box regression accuracy in complex backgrounds, and LAMP pruning technology significantly reduces the model's computational and storage overhead. Experimental results show that the improved algorithm achieves an mAP@0.5 of 85.4%, a 4.1% improvement compared to the original YOLOv8n, while maintaining a low parameter count and computational complexity, demonstrating both high accuracy and efficiency. This research provides a valuable reference for the design and application of lightweight target detection models in the intelligent inspection of power equipment. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: enr DbLabel: Energy & Power Source An: 194141420 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Transmission Line Insulator Defect Detection Method Based on YOLO-MLSL Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zheng%2C+Renhao%22">Zheng, Renhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 17775029211@163.com</i><br /><searchLink fieldCode="AR" term="%22Duan%2C+Guoyong%22">Duan, Guoyong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Xin%22">Cao, Xin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Haofeng%22">Wang, Haofeng</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2305. 21p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br />*<searchLink fieldCode="DE" term="%22Loss+functions+%28Statistics%29%22">Loss functions (Statistics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To address the challenges of insufficient small target recognition, difficulty in edge information extraction, and high computational overhead in insulator defect detection, this paper proposes a lightweight detection method based on the YOLO-MLSL model for transmission line insulator defect detection. First, a C2f-LRFCA module is introduced, effectively enhancing feature interaction through a long-range convolutional attention mechanism, thereby improving the perception of fine-grained defects. Second, an MEUM multi-scale feature enhancement module is designed to achieve more efficient contextual information fusion during upsampling, improving the detection performance for multi-scale targets. Third, the ShapeIoU loss function is employed to improve the bounding box regression accuracy in complex backgrounds, and LAMP pruning technology significantly reduces the model's computational and storage overhead. Experimental results show that the improved algorithm achieves an mAP@0.5 of 85.4%, a 4.1% improvement compared to the original YOLOv8n, while maintaining a low parameter count and computational complexity, demonstrating both high accuracy and efficiency. This research provides a valuable reference for the design and application of lightweight target detection models in the intelligent inspection of power equipment. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141420 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19102305 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 2305 Subjects: – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Loss functions (Statistics) Type: general Titles: – TitleFull: Transmission Line Insulator Defect Detection Method Based on YOLO-MLSL Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zheng, Renhao – PersonEntity: Name: NameFull: Duan, Guoyong – PersonEntity: Name: NameFull: Cao, Xin – PersonEntity: Name: NameFull: Wang, Haofeng IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 10 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
