ICU Equipment Interface Image Recognition Dataset

In modern medical environments, ICU equipment interface recognition faces challenges such as equipment diversity and interface complexity. Currently, most solutions rely on manual operation and monitoring, which are inefficient and prone to errors. This dataset aims to improve the accuracy and efficiency of equipment interface recognition through automated image recognition technology, meeting the medical industry's need for efficient and reliable image processing. The dataset is collected by using high-definition camera equipment in hospital ICU environments to capture the interfaces of various common monitoring devices. A strict multi-round annotation and consistency check are adopted, with the annotation team composed of medical imaging professionals to ensure accurate annotation of medical information. The data preprocessing includes image denoising, cropping, and standardization. Data is stored in JPG format and organized by device type and usage scenario.