Electric Wheelchair Object Detection Image Dataset

The current medical industry faces the challenges of an aging society and disabled groups, and the demand for electric wheelchairs is rising. However, existing object detection technology has a low recognition accuracy in complex environments, especially in dynamic scenes. This dataset aims to improve the accuracy and robustness of object detection algorithms through a wealth of electric wheelchair image data to meet the intelligent needs of assistive medical devices. Data collection was conducted using high-resolution cameras in real scenarios at hospitals and rehabilitation centers, ensuring data diversity and authenticity. To ensure data quality, multiple rounds of annotation and expert review mechanisms were used to ensure annotation consistency and accuracy. The data is stored in JPEG format and organized by folders to facilitate subsequent processing and use.