PBD : Plastic Bottle Dataset for Defect Detection

Abstract

Plastic bottles are widely used in various aspects of daily life, commonly for the storage of liquids in food and beverage packaging, pharmaceutical products, cosmetics, and personal care items. This widespread use places higher demands on the production quality of plastic bottles. Effective defect detection technologies are needed to optimize the plastic bottle manufacturing process, but the development of such technologies has been hindered by the lack of publicly available datasets. To address this issue, we propose two dedicated datasets for detecting defects in plastic bottles: one for bottle bodies and the other for shoulders. The plastic bottle body dataset consists of 500 images, captured from a professional workstation, containing four types of defects. The plastic bottle shoulder dataset consists of 3,463 images of shoulders with various types and colors, captured under the same conditions, and includes two types of defects as well as neck positioning marks. All data were collected using professional imaging equipment. In addition, we conducted benchmark experiments using common object detection models on the proposed datasets to demonstrate their usability in the field of defect detection. The datasets and related code will be made publicly available at https://github.com/lzy-coder/PBD.