Electric Kettle Lid Closure Anomaly Detection Dataset

In the current industrial field of electric kettle production, there are issues with incomplete lid closure leading to steam leakage, posing a risk of scalding users. Existing detection solutions mostly rely on manual inspection, which is inefficient and prone to errors. This dataset aims to assist researchers and engineers in developing automated anomaly detection algorithms by providing a large number of annotated electric kettle lid images, thereby improving detection accuracy and efficiency. Data collection was performed using high-resolution cameras in standardized lighting environments to ensure clear and interference-free images. In terms of quality control, we employed multiple rounds of annotation and consistency checks to ensure the accuracy and reliability of annotations. The data is stored in JPG format and organized by category for ease of subsequent processing and model training.