2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images
Recently, intelligence-based hotspot detection has been widely used in solar photovoltaic (PV) image applications. However, the semantic segmentation approach has limitations in terms of accuracy, particularly for hotspot thermal images. This study introduces a novel method based on Two-tier Semanti...
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| Format: | Article |
| Language: | English |
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IEEE
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/45020/ http://umpir.ump.edu.my/id/eprint/45020/1/2TSS-%20Two-tier%20semantic%20segmentation%20framework%20with%20enhancement.pdf |
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| author | Nurul Huda, Ishak Iza Sazanita, Isa Muhammad Khusairi, Osman Mohd Shawal, Jadin Kamarulazhar, Daud Mohd Zulhamdy, Ab Hamid |
| author_facet | Nurul Huda, Ishak Iza Sazanita, Isa Muhammad Khusairi, Osman Mohd Shawal, Jadin Kamarulazhar, Daud Mohd Zulhamdy, Ab Hamid |
| author_sort | Nurul Huda, Ishak |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Recently, intelligence-based hotspot detection has been widely used in solar photovoltaic (PV) image applications. However, the semantic segmentation approach has limitations in terms of accuracy, particularly for hotspot thermal images. This study introduces a novel method based on Two-tier Semantic Segmentation (2TSS) framework explicitly aimed at enhancing hotspot detection in thermal images of PV modules. The proposed method is designed with two subsequent stages of segmentation models, including image pre-processing at the initial of the framework. The first tier of segmentation distinguishes between solar PV modules and the background, whilst the second tier enhances the hotspot localization region. This research enhances comprehension of multi-tier segmentation architectures in deep learning, focusing on optimizing performance for solar energy systems through comparative analysis of semantic models. Three different segmentation models, namely U-Net, ResNet 18 and ResNet 50 were tested. The ResNet 50 model demonstrated superior segmentation performance across both tiers with 98% and 85% accuracy respectively. In summary, the proposed method demonstrates that applying a combined enhancement algorithm prior to training for hotspot segmentation promotes superior performance with an accuracy improvement of 2.26% over the non-enhancement approach. |
| first_indexed | 2025-11-15T03:57:32Z |
| format | Article |
| id | ump-45020 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:57:32Z |
| publishDate | 2025 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-450202025-07-07T01:05:25Z http://umpir.ump.edu.my/id/eprint/45020/ 2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images Nurul Huda, Ishak Iza Sazanita, Isa Muhammad Khusairi, Osman Mohd Shawal, Jadin Kamarulazhar, Daud Mohd Zulhamdy, Ab Hamid QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Recently, intelligence-based hotspot detection has been widely used in solar photovoltaic (PV) image applications. However, the semantic segmentation approach has limitations in terms of accuracy, particularly for hotspot thermal images. This study introduces a novel method based on Two-tier Semantic Segmentation (2TSS) framework explicitly aimed at enhancing hotspot detection in thermal images of PV modules. The proposed method is designed with two subsequent stages of segmentation models, including image pre-processing at the initial of the framework. The first tier of segmentation distinguishes between solar PV modules and the background, whilst the second tier enhances the hotspot localization region. This research enhances comprehension of multi-tier segmentation architectures in deep learning, focusing on optimizing performance for solar energy systems through comparative analysis of semantic models. Three different segmentation models, namely U-Net, ResNet 18 and ResNet 50 were tested. The ResNet 50 model demonstrated superior segmentation performance across both tiers with 98% and 85% accuracy respectively. In summary, the proposed method demonstrates that applying a combined enhancement algorithm prior to training for hotspot segmentation promotes superior performance with an accuracy improvement of 2.26% over the non-enhancement approach. IEEE 2025 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/45020/1/2TSS-%20Two-tier%20semantic%20segmentation%20framework%20with%20enhancement.pdf Nurul Huda, Ishak and Iza Sazanita, Isa and Muhammad Khusairi, Osman and Mohd Shawal, Jadin and Kamarulazhar, Daud and Mohd Zulhamdy, Ab Hamid (2025) 2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images. IEEE Access, 13. pp. 88888-88904. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2025.3570974 https://doi.org/10.1109/ACCESS.2025.3570974 |
| spellingShingle | QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Nurul Huda, Ishak Iza Sazanita, Isa Muhammad Khusairi, Osman Mohd Shawal, Jadin Kamarulazhar, Daud Mohd Zulhamdy, Ab Hamid 2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images |
| title | 2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images |
| title_full | 2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images |
| title_fullStr | 2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images |
| title_full_unstemmed | 2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images |
| title_short | 2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images |
| title_sort | 2tss: two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images |
| topic | QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/45020/ http://umpir.ump.edu.my/id/eprint/45020/ http://umpir.ump.edu.my/id/eprint/45020/ http://umpir.ump.edu.my/id/eprint/45020/1/2TSS-%20Two-tier%20semantic%20segmentation%20framework%20with%20enhancement.pdf |