Mercury Glass Thermometer Object Detection Image Dataset

In the current medical industry, thermometers are widely used, but traditional manual detection methods are inefficient and prone to errors. Existing object detection technologies are not yet widely applied in medical image analysis, particularly limited in the rapid identification and classification of specific objects. This dataset aims to solve the problem of automatic recognition and localization of mercury glass thermometers in various scenarios, thereby enhancing the efficiency of medical equipment usage. The dataset structure includes image data and corresponding annotation information, collected using high-resolution image capture devices, shot in standard medical environments. In terms of data quality control, multiple rounds of annotation and expert review are conducted to ensure consistency and accuracy of the annotations. The data is stored in JPEG format, organized in folders containing images and annotation information for easy subsequent processing and use.