Air Purifier Button Area Detection Dataset

The current retail e-commerce industry faces an increasing demand for user interface recognition and interaction experience with smart home products. However, existing products often lack systematic button area detection models, leading to user confusion. Current solutions typically rely on manual annotation, which is inefficient and of varying quality, failing to meet high-precision recognition needs. This dataset aims to provide high-quality button area detection data to enhance the user experience of smart home devices. Data collection uses high-resolution camera equipment and captures images under various lighting conditions to ensure data diversity and representativeness. Multiple rounds of annotation, consistency checks, and expert reviews are performed to ensure the accuracy and consistency of annotations. The data is stored in JPEG format with a clear organizational structure, facilitating subsequent training and analysis. The core advantage of this dataset is its high annotation accuracy (over 95%), consistency of 90%, and high completeness. By introducing new annotation methods and data augmentation techniques, we have significantly improved the generalization ability of the model, with practical application recognition accuracy increasing by more than 15%, greatly meeting the demands of smart home products for enhanced user interaction experience.