A Novel MoCo-Based Self-Supervised Learning Framework for Solar Panel Defect Detection
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...
Saved in:
Main Authors: | Jun Huang, Shamsul Arrieya Ariffin, Yongqiang Chen, Jinghui Lin, Wanting Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10840178/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Green Power in the Garden: A Simple Water Feature Using Photovoltaic Solar Panels
by: Edmund Lee Thralls
Published: (2019-04-01) -
Green Power in the Garden: A Simple Water Feature Using Photovoltaic Solar Panels
by: Edmund Lee Thralls
Published: (2019-04-01) -
ORIENTATION SYSTEM OF PV PANELS AFTER SUN
by: GEORGE EDUARD HOLMAN, et al.
Published: (2017-09-01) -
Deep learning in defects detection of PV modules: A review
by: Katleho Masita, et al.
Published: (2025-01-01) -
ENERGY PRODUCTION OF A HYBRID SOLAR ELECTRIC VEHICLE CHARGING SYSTEM
by: RÓBERT ISTÓK
Published: (2023-05-01)