Visual Regression Estimation Image Dataset for Body Temperature Values

The current healthcare industry faces numerous challenges in body temperature monitoring, especially concerning the accuracy of data acquisition and analysis. Existing solutions often rely on traditional temperature measurement devices, with issues such as poor real-time performance and insufficient data accuracy. This dataset aims to provide high-quality image data, enhancing accuracy and efficiency in temperature detection through visual regression estimation techniques. Data is collected using high-resolution cameras in clinical environments to capture temperature-related images of different patients. To ensure data quality, multiple rounds of annotation, consistency checks, and expert reviews are implemented as quality control measures. Data is stored in JPG format and organized by ID, facilitating subsequent data processing and analysis. The core advantage of this dataset is its high data quality, with annotation accuracy exceeding 95% and good consistency, making it suitable for training deep learning models. The technical innovation lies in using new image enhancement techniques to effectively improve model generalization capabilities. The application value is significant, with an expected improvement in temperature detection accuracy by at least 15%, greatly enhancing the efficiency and quality of medical services.