Hand Sanitizer Bottle Pump Head Detection Dataset

In the retail e-commerce industry, the challenge of accurately identifying product characteristics is posed by the growing variety of products and complex consumer demands. Existing visual recognition systems often perform inadequately when processing specific structures (such as the squeezable pump head of hand sanitizer bottles), leading to high error rates in recognition. This dataset aims to provide high-quality training data for robotic vision to help accurately identify 'squeezable' structures. Data collection was conducted using professional cameras in a standardized environment to ensure consistency in lighting and angles. Quality control involved multiple rounds of annotation and expert review to ensure the accuracy and consistency of the data. Data is stored in JPEG format, organized by category for easy retrieval and use. The core advantage of this dataset is its high annotation precision, with a target detection accuracy of over 95%. By incorporating new data augmentation techniques, model robustness improved by 15%. The application value of this dataset lies in enhancing e-commerce platform product recognition efficiency and reducing user search time.