Spring Recognition and Detection Dataset

The Spring Recognition and Detection Dataset addresses the critical challenges in quality control of precision mechanical assemblies in the industrial sector. Currently, industries face issues with manual inspection methods, which are prone to errors and inconsistencies. Existing automated solutions often lack the robustness required to accurately detect defects in complex mechanical components like springs. This dataset aims to provide high-quality images for training machine learning models that can reliably identify and classify springs in various conditions. The dataset comprises images captured in controlled environments using high-resolution cameras, ensuring clarity and detail. Quality control measures include multi-round annotations, consistency checks among annotators, and expert reviews to ensure labeling accuracy. The data is stored in JPG format, organized by image ID, allowing for easy access and processing.