Mercury Thermometer Damage and Crack Detection Image Dataset

The current medical industry faces numerous challenges in equipment monitoring and safety checks, especially in the use of mercury thermometers, where damage and cracks can pose serious safety hazards. Existing detection solutions often rely on manual inspection, which is not only inefficient but also prone to missed detections and misjudgments. Therefore, constructing a high-quality mercury thermometer damage and crack detection dataset aims to improve detection accuracy and efficiency through deep learning technologies. The dataset includes images of various damaged states of mercury thermometers, captured using professional equipment, ensuring image clarity and authenticity. In terms of quality control measures, a method combining multiple rounds of labeling and expert review is adopted to ensure annotation consistency and accuracy. The data storage format is JPG, organized by category for ease of subsequent analysis and use.