A Gradient Guided Architecture Coupled With Filter Fused Representations for Micro-Crack Detection in Photovoltaic Cell Surfaces

Muhammad Hussain, Tianhua Chen, Sofya Titarenko, Pan Su, Mufti Mahmud

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a shallow architecture based on Convolutional Neural Networks (CNN) for detecting Micro-cracks in Photovoltaic (PV) cells within the manufacturing environment. Based on Electro Luminescence (EL) imaging principles, this research presents a mechanism for determining the number of filters within the convolutional blocks, gradient guided filter tuning (GGFT). Observing the similarity between the original EL images and the filter output images obtained via GGFT, the research further introduces a mechanism for generating PV cell images based on EL Modelling, termed Filter Fused Data Scaling (FFDS). The effectiveness of both techniques is presented by benchmarking our developed architecture against ‘off the shelf’ augmentations and State-of-the-Art (SOTA) networks. The performance criteria was widened to include accuracy, computational, architectural, and post-deployment metrics. The high performance of our architecture in an intensive and wide-scoped evaluation demonstrates the high efficacy of our proposed mechanisms for developing PV-specific architectures and addressing the issue of data scarcity, particularly the difficulty in the procurement of quality EL images from the manufacturing site.
Original languageEnglish
Pages (from-to)58950-58964
Number of pages15
JournalIEEE Access
Volume10
Early online date27 May 2022
DOIs
Publication statusPublished - 8 Jun 2022

Fingerprint

Dive into the research topics of 'A Gradient Guided Architecture Coupled With Filter Fused Representations for Micro-Crack Detection in Photovoltaic Cell Surfaces'. Together they form a unique fingerprint.

Cite this