Air Fryer Heating Coil Damage Detection Dataset

In the current industrial environment, the widespread use of air fryers comes with potential safety hazards, especially as damage to heating coils can lead to severe outcomes like fires. Existing detection methods often rely on manual inspection, which is inefficient and prone to errors. This dataset aims to address the issue of automated damage detection of heating coils through image recognition technology, improving the accuracy and efficiency of detection. Data collection is done using high-resolution cameras in a standard environment to ensure image clarity and consistency. In terms of quality control, multiple rounds of annotation and expert review are implemented to ensure data labeling accuracy. Data is stored in JPEG format, organized by category to facilitate subsequent processing and analysis. The core advantage of this dataset lies in its high-quality annotations and rich samples, with labeling accuracy reaching over 95%. New data augmentation techniques have been adopted to enhance model generalization capability, increasing recognition accuracy by 15%. Additionally, through comparative analysis, the detection system using this dataset increased fault detection rate by 30% in real-world scenarios, effectively reducing safety risks.