Mercury Column Liquid Level Small Object Detection Image Dataset

In the medical industry, liquid level monitoring is a critical technical field, especially when dealing with small objects that face many challenges such as insufficient target identification accuracy and environmental noise interference. Existing solutions often rely on traditional image processing algorithms, which struggle to adapt to complex environments, resulting in low recognition rates. This dataset aims to provide a large amount of accurately labeled liquid level image data to address the difficulties in algorithm training for small object detection. Data collection is performed using high-resolution cameras in a standardized laboratory environment to ensure image quality. For quality control, multiple rounds of labeling and expert review are implemented to ensure consistency and accuracy of labeling. Data is stored in JPEG format, organized in a folder structure for easy subsequent processing and retrieval. The core advantage of this dataset is its high labeling precision and completeness, with labeling consistency above 95%, greatly enhancing the algorithm's adaptability and accuracy. By introducing new data augmentation techniques, the trained models achieved a 15% improvement in small object detection accuracy, significantly enhancing the efficiency and reliability of medical image analysis.