Image processing‐based noise‐resilient insulator defect detection using YOLOv8x
Abstract Accurate and efficient insulator defect detection is critical for power grid reliability, but it can be affected by the presence of noises in captured images and can be difficult to employ for real‐time operation due to the slow processing of the detection scheme. This paper proposes a nove...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Smart Grid |
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| Online Access: | https://doi.org/10.1049/stg2.12199 |
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| author | Shagor Hasan Md. Abdur Rahman Md. Rashidul Islam Animesh Sarkar Tusher |
| author_facet | Shagor Hasan Md. Abdur Rahman Md. Rashidul Islam Animesh Sarkar Tusher |
| author_sort | Shagor Hasan |
| collection | DOAJ |
| description | Abstract Accurate and efficient insulator defect detection is critical for power grid reliability, but it can be affected by the presence of noises in captured images and can be difficult to employ for real‐time operation due to the slow processing of the detection scheme. This paper proposes a novel framework based on the YOLOv8x object detection scheme, addressing the challenge of detecting small defects in complex aerial images and providing a noise mitigation scheme. A Gaussian blur and Laplacian sharpening‐based hybrid scheme is proposed to mitigate the impacts of noises in insulator images. Experimental results indicate that the proposed framework can achieve a mean average precision (mAP) of 98.4% on noise‐free images, surpassing benchmark models, such as YOLOv5x and YOLOv7 by 2.1% and 3.9%, respectively. Also, while the performance of a conventional system can decrease to a mAP of 93.3% in the worst case, the implementation of the proposed mitigation scheme ensures a mAP of 96.7% for that case. With an inference speed of 56.9 ms per image, this approach offers a promising solution for real‐time power line inspection, contributing to enhanced power grid maintenance and safety. |
| format | Article |
| id | doaj-art-4cd4dd7230dd44d682a253f008ce18ef |
| institution | OA Journals |
| issn | 2515-2947 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj-art-4cd4dd7230dd44d682a253f008ce18ef2025-08-20T02:35:35ZengWileyIET Smart Grid2515-29472024-12-01761036105310.1049/stg2.12199Image processing‐based noise‐resilient insulator defect detection using YOLOv8xShagor Hasan0Md. Abdur Rahman1Md. Rashidul Islam2Animesh Sarkar Tusher3Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi BangladeshDepartment of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi BangladeshDepartment of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi BangladeshDepartment of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi BangladeshAbstract Accurate and efficient insulator defect detection is critical for power grid reliability, but it can be affected by the presence of noises in captured images and can be difficult to employ for real‐time operation due to the slow processing of the detection scheme. This paper proposes a novel framework based on the YOLOv8x object detection scheme, addressing the challenge of detecting small defects in complex aerial images and providing a noise mitigation scheme. A Gaussian blur and Laplacian sharpening‐based hybrid scheme is proposed to mitigate the impacts of noises in insulator images. Experimental results indicate that the proposed framework can achieve a mean average precision (mAP) of 98.4% on noise‐free images, surpassing benchmark models, such as YOLOv5x and YOLOv7 by 2.1% and 3.9%, respectively. Also, while the performance of a conventional system can decrease to a mAP of 93.3% in the worst case, the implementation of the proposed mitigation scheme ensures a mAP of 96.7% for that case. With an inference speed of 56.9 ms per image, this approach offers a promising solution for real‐time power line inspection, contributing to enhanced power grid maintenance and safety.https://doi.org/10.1049/stg2.12199artificial intelligence and data analyticsfeature extractionpattern classificationpower system reliabilitypower transmission |
| spellingShingle | Shagor Hasan Md. Abdur Rahman Md. Rashidul Islam Animesh Sarkar Tusher Image processing‐based noise‐resilient insulator defect detection using YOLOv8x IET Smart Grid artificial intelligence and data analytics feature extraction pattern classification power system reliability power transmission |
| title | Image processing‐based noise‐resilient insulator defect detection using YOLOv8x |
| title_full | Image processing‐based noise‐resilient insulator defect detection using YOLOv8x |
| title_fullStr | Image processing‐based noise‐resilient insulator defect detection using YOLOv8x |
| title_full_unstemmed | Image processing‐based noise‐resilient insulator defect detection using YOLOv8x |
| title_short | Image processing‐based noise‐resilient insulator defect detection using YOLOv8x |
| title_sort | image processing based noise resilient insulator defect detection using yolov8x |
| topic | artificial intelligence and data analytics feature extraction pattern classification power system reliability power transmission |
| url | https://doi.org/10.1049/stg2.12199 |
| work_keys_str_mv | AT shagorhasan imageprocessingbasednoiseresilientinsulatordefectdetectionusingyolov8x AT mdabdurrahman imageprocessingbasednoiseresilientinsulatordefectdetectionusingyolov8x AT mdrashidulislam imageprocessingbasednoiseresilientinsulatordefectdetectionusingyolov8x AT animeshsarkartusher imageprocessingbasednoiseresilientinsulatordefectdetectionusingyolov8x |