Rice Cooker Shell Injection Mark Detection Dataset

In the current industrial landscape, the manufacturing of rice cooker shells faces challenges such as aesthetic defects caused by injection molding, including drag marks and cold spots. Existing solutions often rely on manual inspection, which is time-consuming and prone to human error. This dataset aims to address the need for automated defect detection using machine learning techniques, thereby improving efficiency and accuracy. The dataset includes images collected from various production environments using high-resolution cameras, ensuring a diverse range of samples. Quality control measures include multiple rounds of annotation by trained professionals and consistency checks to ensure high accuracy. The data is stored in JPG format, organized with clear labeling for each defect type. The core advantages of this dataset lie in its high-quality annotations, achieving over 95% accuracy in defect classification. Innovative labeling methods, such as using pixel-level annotations, enhance the dataset's value for training advanced models. The dataset addresses significant industry challenges by reducing inspection time by up to 60% and improving defect detection rates by over 25%, making it a valuable resource for manufacturers aiming to enhance product quality.