Fuse Box Cover Detection Dataset

The current industrial sector faces significant challenges in ensuring the safety and reliability of electrical components, particularly in after-sales maintenance and circuit protection checks. Existing solutions often lack precision and fail to address the variability in fuse box designs, leading to inconsistent inspection results. This dataset aims to provide a robust foundation for developing advanced machine learning models that can accurately detect and classify various conditions of fuse box covers. Data is collected using high-resolution cameras in controlled environments, ensuring optimal lighting and minimal distractions. Quality control measures include multi-round annotations, consistency checks across different annotators, and expert reviews to verify labeling accuracy. The data is organized in JPG format with systematic folder structures for easy access and processing.