Hybrid YOLOv8 and Fast R-CNN for Accurate Schematic Detection in Power Distribution Networks
Accurate schematic detection in Power Distribution Networks (PDNs) is critical for effective fault detection, asset management, and predictive maintenance. Conventional edge detection methods often struggle with the complexity and scale of modern PDNs, while standalone deep learning approaches face...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10966861/ |
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| author | Aviv Yuniar Rahman Zuhaina Zakaria |
| author_facet | Aviv Yuniar Rahman Zuhaina Zakaria |
| author_sort | Aviv Yuniar Rahman |
| collection | DOAJ |
| description | Accurate schematic detection in Power Distribution Networks (PDNs) is critical for effective fault detection, asset management, and predictive maintenance. Conventional edge detection methods often struggle with the complexity and scale of modern PDNs, while standalone deep learning approaches face challenges in balancing real-time performance and high precision. To address these limitations, this paper introduces a hybrid detection framework that synergizes YOLOv8 and Fast R-CNN, leveraging their complementary strengths. YOLOv8 enables rapid initial detections with real-time capabilities, while Fast R-CNN refines these outputs to enhance contextual accuracy. A key contribution of this work is the integration of ensemble techniques—Soft Voting, Hard Voting, and Weighted Average Voting— which further optimize detection performance by effectively aggregating predictions from both models. Using a curated dataset of 3,304 schematic images, the proposed method achieves state-of-the-art results, including a precision of 96.59%, recall of 97.27%, and mean Average Precision at an Intersection over Union (IoU) threshold of 0.50 (mAP@50) of 99.06% with the Hard Voting ensemble. These findings underscore the robustness, scalability, and applicability of the proposed framework in automating schematic analysis. Furthermore, the method demonstrates strong potential for practical deployment within PLN Indonesia, particularly in real-time fault detection, technician training, and predictive maintenance, contributing to enhanced reliability and operational efficiency in national power distribution systems. |
| format | Article |
| id | doaj-art-f12f101f5ea34cd58bda47201a599e89 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f12f101f5ea34cd58bda47201a599e892025-08-20T02:18:55ZengIEEEIEEE Access2169-35362025-01-0113694256943710.1109/ACCESS.2025.356127910966861Hybrid YOLOv8 and Fast R-CNN for Accurate Schematic Detection in Power Distribution NetworksAviv Yuniar Rahman0https://orcid.org/0000-0002-2316-2980Zuhaina Zakaria1https://orcid.org/0000-0002-6934-9775School of Graduate Studies, Asia e University, Subang Jaya, MalaysiaSchool of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam, MalaysiaAccurate schematic detection in Power Distribution Networks (PDNs) is critical for effective fault detection, asset management, and predictive maintenance. Conventional edge detection methods often struggle with the complexity and scale of modern PDNs, while standalone deep learning approaches face challenges in balancing real-time performance and high precision. To address these limitations, this paper introduces a hybrid detection framework that synergizes YOLOv8 and Fast R-CNN, leveraging their complementary strengths. YOLOv8 enables rapid initial detections with real-time capabilities, while Fast R-CNN refines these outputs to enhance contextual accuracy. A key contribution of this work is the integration of ensemble techniques—Soft Voting, Hard Voting, and Weighted Average Voting— which further optimize detection performance by effectively aggregating predictions from both models. Using a curated dataset of 3,304 schematic images, the proposed method achieves state-of-the-art results, including a precision of 96.59%, recall of 97.27%, and mean Average Precision at an Intersection over Union (IoU) threshold of 0.50 (mAP@50) of 99.06% with the Hard Voting ensemble. These findings underscore the robustness, scalability, and applicability of the proposed framework in automating schematic analysis. Furthermore, the method demonstrates strong potential for practical deployment within PLN Indonesia, particularly in real-time fault detection, technician training, and predictive maintenance, contributing to enhanced reliability and operational efficiency in national power distribution systems.https://ieeexplore.ieee.org/document/10966861/Hybrid detectionYOLOv8fast R-CNNschematic detectionpower distribution networks |
| spellingShingle | Aviv Yuniar Rahman Zuhaina Zakaria Hybrid YOLOv8 and Fast R-CNN for Accurate Schematic Detection in Power Distribution Networks IEEE Access Hybrid detection YOLOv8 fast R-CNN schematic detection power distribution networks |
| title | Hybrid YOLOv8 and Fast R-CNN for Accurate Schematic Detection in Power Distribution Networks |
| title_full | Hybrid YOLOv8 and Fast R-CNN for Accurate Schematic Detection in Power Distribution Networks |
| title_fullStr | Hybrid YOLOv8 and Fast R-CNN for Accurate Schematic Detection in Power Distribution Networks |
| title_full_unstemmed | Hybrid YOLOv8 and Fast R-CNN for Accurate Schematic Detection in Power Distribution Networks |
| title_short | Hybrid YOLOv8 and Fast R-CNN for Accurate Schematic Detection in Power Distribution Networks |
| title_sort | hybrid yolov8 and fast r cnn for accurate schematic detection in power distribution networks |
| topic | Hybrid detection YOLOv8 fast R-CNN schematic detection power distribution networks |
| url | https://ieeexplore.ieee.org/document/10966861/ |
| work_keys_str_mv | AT avivyuniarrahman hybridyolov8andfastrcnnforaccurateschematicdetectioninpowerdistributionnetworks AT zuhainazakaria hybridyolov8andfastrcnnforaccurateschematicdetectioninpowerdistributionnetworks |