Infrared thermography is increasingly applied in sports science due to promising observations regarding changes in skin's surface radiation temperature ( Tsr) before, during, and after exercise. The common manual thermogram analysis limits an objective and reproducible measurement of Tsr. Previous analysis approaches depend on expert knowledge and have not been applied during movement. We aimed to develop a deep neural network (DNN) capable of automatically and objectively segmenting body parts, recognizing blood vessel-associated Tsr distributions, and continuously measuring Tsr during exercise. We conducted 38 cardiopulmonary exercise tests on a treadmill. We developed two DNNs: body part network and vessel network, to perform semantic segmentation of 1 107 855 thermal images. Both DNNs were trained with 263 training and 75 validation images. Additionally, we compare the results of a common manual thermogram analysis with these of the DNNs. Performance analysis identified a mean IoU of 0.8 for body part network and 0.6 for vessel network. There is a high agreement between manual and automatic analysis (r = 0.999; p 0.001; T-test: p = 0.116), with a mean difference of 0.01 °C (0.08). Non-parametric Bland Altman's analysis showed that the 95% agreement ranges between - 0.086 °C and 0.228 °C. The developed DNNs enable automatic, objective, and continuous measurement of Tsr and recognition of blood vessel-associated Tsr distributions in resting and moving legs. Hence, the DNNs surpass previous algorithms by eliminating manual region of interest selection and form the currently needed foundation to extensively investigate Tsr distributions related to non-invasive diagnostics of (patho-)physiological traits in means of exercise radiomics.