An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm
To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation algorithm has been proposed in this paper. First...
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| Format: | Article |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/19/11031 |
| _version_ | 1797576158493016064 |
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| author | Fangbin Wang Zini Wang Zhong Chen Darong Zhu Xue Gong Wanlin Cong |
| author_facet | Fangbin Wang Zini Wang Zhong Chen Darong Zhu Xue Gong Wanlin Cong |
| author_sort | Fangbin Wang |
| collection | DOAJ |
| description | To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation algorithm has been proposed in this paper. Firstly, the edge image features of hot spots were extracted based on residual neural networks. Secondly, by combining the feature pyramid structure, an edge-guided feature pyramid structure was designed, and the hot spot edge features were injected into a Mask R-CNN network. Thirdly, an infrared spatial attention module was introduced into the Mask R-CNN network when feature extraction and the infrared features of the detected hot spots were enhanced. Fourthly, the size ratio of the candidate frames was adjusted self-adaptively according to the structural characteristics of the aspect ratio of the hot spots. Finally, the validation experiments were conducted, and the results demonstrated that the hot spot contours of thermal infrared images were enhanced through the algorithm proposed in this paper, and the segmentation accuracy was significantly improved. |
| first_indexed | 2024-03-10T21:48:17Z |
| format | Article |
| id | doaj.art-1638e3396ac14da492d739cde377f9b8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:48:17Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1638e3396ac14da492d739cde377f9b82023-11-19T14:07:39ZengMDPI AGApplied Sciences2076-34172023-10-0113191103110.3390/app131911031An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation AlgorithmFangbin Wang0Zini Wang1Zhong Chen2Darong Zhu3Xue Gong4Wanlin Cong5School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230601, ChinaSchool of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230601, ChinaSchool of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230601, ChinaSchool of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230601, ChinaSchool of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230601, ChinaUltra High Voltage Branch, State Grid Anhui Electric Power Co., Hefei Anhui Ltd., Hefei 230041, ChinaTo overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation algorithm has been proposed in this paper. Firstly, the edge image features of hot spots were extracted based on residual neural networks. Secondly, by combining the feature pyramid structure, an edge-guided feature pyramid structure was designed, and the hot spot edge features were injected into a Mask R-CNN network. Thirdly, an infrared spatial attention module was introduced into the Mask R-CNN network when feature extraction and the infrared features of the detected hot spots were enhanced. Fourthly, the size ratio of the candidate frames was adjusted self-adaptively according to the structural characteristics of the aspect ratio of the hot spots. Finally, the validation experiments were conducted, and the results demonstrated that the hot spot contours of thermal infrared images were enhanced through the algorithm proposed in this paper, and the segmentation accuracy was significantly improved.https://www.mdpi.com/2076-3417/13/19/11031photovoltaic hot spotimage segmentationdeep learningedge guidance |
| spellingShingle | Fangbin Wang Zini Wang Zhong Chen Darong Zhu Xue Gong Wanlin Cong An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm Applied Sciences photovoltaic hot spot image segmentation deep learning edge guidance |
| title | An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm |
| title_full | An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm |
| title_fullStr | An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm |
| title_full_unstemmed | An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm |
| title_short | An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm |
| title_sort | edge guided deep learning solar panel hotspot thermal image segmentation algorithm |
| topic | photovoltaic hot spot image segmentation deep learning edge guidance |
| url | https://www.mdpi.com/2076-3417/13/19/11031 |
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