# ThermoCycleNet: Stereo-based Thermogram Labeling for Model Transition to Cycling

Source: [ThermoCycleNet: Stereo-based Thermogram Labeling for Model Transition to Cycling](<https://www.sota2.com/research/paper/68fa7a521e209131cea8270f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:58:49.616Z

Authors: Daniel Andr\\'es L\\'opez, Vincent Weber, Severin Zentgraf, Barlo Hillen, Perikles Simon, Elmar Sch\\"omer

Published: 2025-08-01T17:55:00.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2508.00974>)

[PDF](<https://arxiv.org/pdf/2508.00974.pdf>)