Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency Normalization
RGB-Thermal (RGBT) semantic segmentation is an emerging technology for identifying objects and materials in high dynamic range scenes. Thermal imaging particularly enhances feature extraction at close range for applications such as textile damage detection. In this paper, we present RGBT-Textile, a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2306 |
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| author | Farshid Rayhan Jitesh Joshi Guangyu Ren Lucie Hernandez Bruna Petreca Sharon Baurley Nadia Berthouze Youngjun Cho |
| author_facet | Farshid Rayhan Jitesh Joshi Guangyu Ren Lucie Hernandez Bruna Petreca Sharon Baurley Nadia Berthouze Youngjun Cho |
| author_sort | Farshid Rayhan |
| collection | DOAJ |
| description | RGB-Thermal (RGBT) semantic segmentation is an emerging technology for identifying objects and materials in high dynamic range scenes. Thermal imaging particularly enhances feature extraction at close range for applications such as textile damage detection. In this paper, we present RGBT-Textile, a novel dataset specifically developed for close-range textile and damage segmentation. We meticulously designed the data collection protocol, software tools, and labeling process in collaboration with textile scientists. Additionally, we introduce ThermoFreq, a novel thermal frequency normalization method that reduces temperature noise effects in segmentation tasks. We evaluate our dataset alongside six existing RGBT datasets using state-of-the-art (SOTA) models. Experimental results demonstrate the superior performance of the SOTA models with ThermoFreq, highlighting its effectiveness in addressing noise challenges inherent in RGBT semantic segmentation across diverse environmental conditions. We make our dataset publicly accessible to foster further research and collaborations. |
| format | Article |
| id | doaj-art-40c298c9a5a249588dc98ba4d0c3e2a2 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-40c298c9a5a249588dc98ba4d0c3e2a22025-08-20T02:15:54ZengMDPI AGSensors1424-82202025-04-01257230610.3390/s25072306Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency NormalizationFarshid Rayhan0Jitesh Joshi1Guangyu Ren2Lucie Hernandez3Bruna Petreca4Sharon Baurley5Nadia Berthouze6Youngjun Cho7Department of Computer Science, University College London, London NW1 2AE, UKDepartment of Computer Science, University College London, London NW1 2AE, UKDepartment of Computer Science, University College London, London NW1 2AE, UKMaterials Science Research Centre, Royal College of Art, London SW7 2EU, UKMaterials Science Research Centre, Royal College of Art, London SW7 2EU, UKMaterials Science Research Centre, Royal College of Art, London SW7 2EU, UKDepartment of Computer Science, University College London, London NW1 2AE, UKDepartment of Computer Science, University College London, London NW1 2AE, UKRGB-Thermal (RGBT) semantic segmentation is an emerging technology for identifying objects and materials in high dynamic range scenes. Thermal imaging particularly enhances feature extraction at close range for applications such as textile damage detection. In this paper, we present RGBT-Textile, a novel dataset specifically developed for close-range textile and damage segmentation. We meticulously designed the data collection protocol, software tools, and labeling process in collaboration with textile scientists. Additionally, we introduce ThermoFreq, a novel thermal frequency normalization method that reduces temperature noise effects in segmentation tasks. We evaluate our dataset alongside six existing RGBT datasets using state-of-the-art (SOTA) models. Experimental results demonstrate the superior performance of the SOTA models with ThermoFreq, highlighting its effectiveness in addressing noise challenges inherent in RGBT semantic segmentation across diverse environmental conditions. We make our dataset publicly accessible to foster further research and collaborations.https://www.mdpi.com/1424-8220/25/7/2306RGB-Thermal datasettextile damage detectionsemantic segmentation |
| spellingShingle | Farshid Rayhan Jitesh Joshi Guangyu Ren Lucie Hernandez Bruna Petreca Sharon Baurley Nadia Berthouze Youngjun Cho Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency Normalization Sensors RGB-Thermal dataset textile damage detection semantic segmentation |
| title | Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency Normalization |
| title_full | Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency Normalization |
| title_fullStr | Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency Normalization |
| title_full_unstemmed | Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency Normalization |
| title_short | Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency Normalization |
| title_sort | advancing textile damage segmentation a novel rgbt dataset and thermal frequency normalization |
| topic | RGB-Thermal dataset textile damage detection semantic segmentation |
| url | https://www.mdpi.com/1424-8220/25/7/2306 |
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