A Novel MoCo-Based Self-Supervised Learning Framework for Solar Panel Defect Detection

doi:10.1109/ACCESS.2025.3529701

A Novel MoCo-Based Self-Supervised Learning Framework for Solar Panel Defect Detection

2025-01-01

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OA

AI

J

Jun Huang

S

Shamsul Arrieya Ariffin

DOI：10.1109/ACCESS.2025.3529701

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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.

Keywords:

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

A Novel MoCo-Based Self-Supervised Learning Framework for Solar Panel Defect Detection

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