.png){kind=icon}
返回
A Novel MoCo-Based Self-Supervised Learning Framework for Solar Panel Defect Detection
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2025.3529701.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Defect detection in solar panels remains constrained by the limitations of manual labeling and the inefficiency of traditional inspection methods, which often struggle with large, high-resolution imagery. This study presents a novel self-supervised learning approach using the Momentum Contrast (MoCo) framework to address these challenges without relying on annotated datasets. Leveraging MoCo's robust feature extraction and K-Nearest Neighbors (KNN) clustering, our method achieves accurate defect identification, bypassing the dependency on labeled data. Evaluated on the ELPV dataset, our approach attained a notable 96.95% accuracy, demonstrating significant improvement over existing unsupervised methods like KDAD, SAOE, DRA, and BGAD-FAS, and even outperforming some supervised models such as Adapted VGG19, Adapted VGG16, and ShuffleNet. Additionally, our model achieved 99.44% accuracy on the EL dataset, underscoring its adaptability and robustness across different environments. This framework offers a scalable, automated solution for image-level defect detection, poised to enhance efficiency and reduce manual intervention in industrial applications.
Keyword:
Defect detection
Feature extraction
Solar panels
Accuracy
Attention mechanisms
Adaptation models
Nearest neighbor methods
Photovoltaic cells
Computational efficiency
Training
Solar panel defect detection
self-supervised learning
MoCo
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Detecting Anomalies in Attributed Networks Through Sparse Canonical Correlation Analysis Combined With Random Masking and Padding.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Analysis of Canopy Gaps of Coastal Broadleaf Forest Plantations in Northeast Taiwan Using UAV Lidar and the Weibull Distribution利用UAV Lidar和Weibull分布对台湾东北部沿海阔叶林人工林冠层间隙的分析OAAI
OAAI分享收藏Long-term (2005–2015) trend analysis of PM2.5 precursor gas NO2 and SO2 concentrations in Taiwan
PREAI
分享收藏
查看更多内容