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
-
Solar photovoltaic panel cells defects classification using deep learning ensemble methods
by: H. Tella, et al.
Published: (2025-02-01) -
Comparative analysis of solar panel output power with variations of Heatsink type cooling systems
by: Dwi Yulia Handayani, et al.
Published: (2023-12-01) -
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) -
Optimization Design for Support Points of the Body-Mounted Solar Panel
by: Qingwu Liu, et al.
Published: (2024-12-01)