ThermoCycleNet: Stereo-based Thermogram Labeling for Model Transition to Cycling
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913971447005184 |
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| author | López, Daniel Andrés Weber, Vincent Zentgraf, Severin Hillen, Barlo Simon, Perikles Schömer, Elmar |
| author_facet | López, Daniel Andrés Weber, Vincent Zentgraf, Severin Hillen, Barlo Simon, Perikles Schömer, Elmar |
| contents | Infrared thermography is emerging as a powerful tool in sports medicine, allowing assessment of thermal radiation during exercise and analysis of anatomical regions of interest, such as the well-exposed calves. Building on our previous advanced automatic annotation method, we aimed to transfer the stereo- and multimodal-based labeling approach from treadmill running to ergometer cycling. Therefore, the training of the semantic segmentation network with automatic labels and fine-tuning on high-quality manually annotated images has been examined and compared in different data set combinations. The results indicate that fine-tuning with a small fraction of manual data is sufficient to improve the overall performance of the deep neural network. Finally, combining automatically generated labels with small manually annotated data sets accelerates the adaptation of deep neural networks to new use cases, such as the transition from treadmill to bicycle. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_00974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ThermoCycleNet: Stereo-based Thermogram Labeling for Model Transition to Cycling López, Daniel Andrés Weber, Vincent Zentgraf, Severin Hillen, Barlo Simon, Perikles Schömer, Elmar Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Infrared thermography is emerging as a powerful tool in sports medicine, allowing assessment of thermal radiation during exercise and analysis of anatomical regions of interest, such as the well-exposed calves. Building on our previous advanced automatic annotation method, we aimed to transfer the stereo- and multimodal-based labeling approach from treadmill running to ergometer cycling. Therefore, the training of the semantic segmentation network with automatic labels and fine-tuning on high-quality manually annotated images has been examined and compared in different data set combinations. The results indicate that fine-tuning with a small fraction of manual data is sufficient to improve the overall performance of the deep neural network. Finally, combining automatically generated labels with small manually annotated data sets accelerates the adaptation of deep neural networks to new use cases, such as the transition from treadmill to bicycle. |
| title | ThermoCycleNet: Stereo-based Thermogram Labeling for Model Transition to Cycling |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.00974 |