YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization
Reliable photovoltaic (PV) module defect detection is essential for maintaining long term energy efficiency and lowering solar power system maintenance costs. The deep learning model presented in this research is based on a hybridized YOLOv8n architecture and is lightweight and high performing. It i...
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2025-01-01
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| author | Likitha Reddy Yeddula Archana Pallakonda Rayappa David Amar Raj Rama Muni Reddy Yanamala K. Krishna Prakasha Mallempati Sunil Kumar |
| author_facet | Likitha Reddy Yeddula Archana Pallakonda Rayappa David Amar Raj Rama Muni Reddy Yanamala K. Krishna Prakasha Mallempati Sunil Kumar |
| author_sort | Likitha Reddy Yeddula |
| collection | DOAJ |
| description | Reliable photovoltaic (PV) module defect detection is essential for maintaining long term energy efficiency and lowering solar power system maintenance costs. The deep learning model presented in this research is based on a hybridized YOLOv8n architecture and is lightweight and high performing. It is designed for multi scale defect identification in a variety of imaging modalities, such as RGB, grayscale, and infrared datasets. The proposed approach combines a BiFPN based neck, Ghost Bottlenecks, and Efficient Channel Attention (ECA) to improve multi scale representation, decrease redundant computation, and increase feature extraction. The model performs better in terms of detection accuracy and efficiency, as shown by experimental findings on three benchmark datasets: PVEL-AD, PV-Multi-Defect, Solar Panel Anomalies. The model’s respective <inline-formula> <tex-math notation="LaTeX">$\text {mAP@50}$ </tex-math></inline-formula> values are 96.5%, 94.6%, and 97.6%. At a steady inference time of only 1.9 ms and 8.1 GFLOPs, it also achieves near-perfect recall (up to 99.0%) and high precision (up to 98.4%). With just 3M parameters, the proposed hybrid model provides a much better accuracy-latency trade-off 61 current state-of-the-art models, which makes it perfect for real-time solar inspection applications, such as edge deployment in drones and embedded systems. The outcomes confirm that reliable PV fault localization under a range of operating situations may be achieved by combining deep feature fusion, lightweight attention, and efficient convolution. |
| format | Article |
| id | doaj-art-6efbd81f6ed043d490f6b5c58537dcdf |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6efbd81f6ed043d490f6b5c58537dcdf2025-08-20T03:33:34ZengIEEEIEEE Access2169-35362025-01-011311401211402810.1109/ACCESS.2025.358424911059241YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect LocalizationLikitha Reddy Yeddula0Archana Pallakonda1https://orcid.org/0009-0005-7865-4156Rayappa David Amar Raj2https://orcid.org/0000-0002-5888-5513Rama Muni Reddy Yanamala3https://orcid.org/0009-0007-9132-4914K. Krishna Prakasha4https://orcid.org/0000-0001-7797-1399Mallempati Sunil Kumar5Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, National Institute of Technology Warangal, Warangal, Telangana, IndiaAmrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, IndiaDepartment of Electronics and Communication Engineering, Indian Institute of Information Technology Design and Manufacturing (IIITD&M), Kancheepuram, Chennai, IndiaSchool of Computer Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, IndiaDepartment of Electrical and Electronics Engineering, DVR & Dr. HS MIC College of Technology, Vijayawada, Andhra Pradesh, IndiaReliable photovoltaic (PV) module defect detection is essential for maintaining long term energy efficiency and lowering solar power system maintenance costs. The deep learning model presented in this research is based on a hybridized YOLOv8n architecture and is lightweight and high performing. It is designed for multi scale defect identification in a variety of imaging modalities, such as RGB, grayscale, and infrared datasets. The proposed approach combines a BiFPN based neck, Ghost Bottlenecks, and Efficient Channel Attention (ECA) to improve multi scale representation, decrease redundant computation, and increase feature extraction. The model performs better in terms of detection accuracy and efficiency, as shown by experimental findings on three benchmark datasets: PVEL-AD, PV-Multi-Defect, Solar Panel Anomalies. The model’s respective <inline-formula> <tex-math notation="LaTeX">$\text {mAP@50}$ </tex-math></inline-formula> values are 96.5%, 94.6%, and 97.6%. At a steady inference time of only 1.9 ms and 8.1 GFLOPs, it also achieves near-perfect recall (up to 99.0%) and high precision (up to 98.4%). With just 3M parameters, the proposed hybrid model provides a much better accuracy-latency trade-off 61 current state-of-the-art models, which makes it perfect for real-time solar inspection applications, such as edge deployment in drones and embedded systems. The outcomes confirm that reliable PV fault localization under a range of operating situations may be achieved by combining deep feature fusion, lightweight attention, and efficient convolution.https://ieeexplore.ieee.org/document/11059241/BiFPNdefect detectionefficient channel attentionghost bottlenecksYOLOv8n |
| spellingShingle | Likitha Reddy Yeddula Archana Pallakonda Rayappa David Amar Raj Rama Muni Reddy Yanamala K. Krishna Prakasha Mallempati Sunil Kumar YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization IEEE Access BiFPN defect detection efficient channel attention ghost bottlenecks YOLOv8n |
| title | YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization |
| title_full | YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization |
| title_fullStr | YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization |
| title_full_unstemmed | YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization |
| title_short | YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization |
| title_sort | yolov8n gbe a hybrid yolov8n model with ghost convolutions and bifpn eca attention for solar pv defect localization |
| topic | BiFPN defect detection efficient channel attention ghost bottlenecks YOLOv8n |
| url | https://ieeexplore.ieee.org/document/11059241/ |
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