International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 4 July-August 2024 Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 4
July-August 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Solar Surface Defect Detection Based on Optimized YOLOv5

Author(s) V SAI TARUN
Country India
Abstract This study introduces an advanced Solar Cell Surface Defect Detection method utilizing an improved YOLO v5, FaserRCNN and YOLOV6 algorithms. Addressing the challenges posed by complex image backgrounds, variable defect morphology, and large-scale differences, our approach incorporates deformable convolution in the CSP module for adaptive learning scale and perceptual field size. The integration of the ECA-Net attention mechanism enhances feature extraction capabilities, while the addition of a tiny defect prediction head improves detection accuracy across different scales. Optimization techniques, including Mosaic and MixUp data augmentation, K-meansCC clustering anchor box algorithm, and the CIOU loss function, contribute to superior model performance. Experimental results demonstrate an impressive accuracy of 97.14% for YOLOv5, outperforming Faster R-CNN's 90.66%. Further extension studies on YOLOv6, YOLOv7, and YOLOv8 reveal YOLOv6 as the most effective, achieving a remarkable accuracy of 98.28%. This research establishes a robust solution for solar cell defect detection, showcasing the efficacy of our proposed algorithm for industrial applications.
Keywords eep learning YOLOv5 solarcelldefectdetection ELimage YoloV6withVGG16
Field Engineering
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-14
Cite This Solar Surface Defect Detection Based on Optimized YOLOv5 - V SAI TARUN - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.19496
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.19496
Short DOI https://doi.org/gtt8w3
View / Download PDF File

Share this


E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 4 Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com