Iron Temperature Control Slider Detection Dataset

In the current retail e-commerce industry, quality monitoring of household appliances like irons faces many challenges, especially in detecting temperature adjustment actions. Traditional manual monitoring methods are inefficient and prone to errors. Existing automated detection solutions often fail to accurately capture the starting point of temperature adjustments, leading to product quality fluctuations and consumer dissatisfaction. This dataset aims to provide reliable training material for object detection models through high-quality image data, addressing the inadequacy in the accuracy of temperature adjustment detection. The dataset structure includes image paths, label information, and timestamps. Data collection was performed using high-resolution cameras in a standard laboratory environment to ensure data clarity and usability. Furthermore, multiple rounds of labeling and expert reviews were implemented to ensure labeling accuracy and consistency. The data will be stored in JPG format and organized chronologically for ease of subsequent analysis and use.