Lightweight Convolutional Network For Automated Photovoltaic Defect Detection

Arsalan Zahid, Muhammad Hussain, Richard Hill, Hussain Al-Aqrabi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


As the World moves towards renewable energy, photovoltaic modules are a fundamental option due to their green nature. However, the manufacturing process of solar cells is complex and vulnerable to discrepancies which can impact the overall performance of the system. Although human-led inspection is seen as the de-facto quality inspection protocol, issues pertaining to bias, cost and time can make it an expensive process. To this effect, this paper focuses on the development of a custom convolutional architecture that is lightweight, hence deployable within manufacturing facilities to assist with defective solar cell inspection. In addition, to address the issue of data scarcity, representative data augmentations are producing tailored towards enhancing the model's generalizability. The high efficacy of the proposed CNN and proposed augmentations can be gauged by the fact that 98% F1 score was achieved overall.

Original languageEnglish
Title of host publication2023 9th International Conference on Information Technology Trends
Subtitle of host publicationITT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350327502
ISBN (Print)9798350327519
Publication statusPublished - 24 Jul 2023
Event9th International IEEE Conference in Information Technology Trends 2023: The Application of AI in Sustainable Computing - HCT-Dubai Men’s campus, Dubai, United Arab Emirates
Duration: 24 May 202325 May 2023
Conference number: 9


Conference9th International IEEE Conference in Information Technology Trends 2023
Abbreviated titleITT 2023
Country/TerritoryUnited Arab Emirates
Internet address

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