CNC Machined Parts Burr Recognition Dataset

In the current industrial sector, the quality control of CNC machined parts faces significant challenges due to the increasing complexity of products and the demand for higher precision. Existing solutions often rely on manual inspection, which is time-consuming and prone to human error. This dataset aims to address the technical challenge of automating burr detection in machined parts to improve efficiency and accuracy. The dataset consists of images captured from various CNC machining processes under controlled lighting conditions. Quality control measures include multiple rounds of annotation, consistency checks among annotators, and expert reviews to ensure high-quality labels. The data is stored in JPG format organized by folders categorized by burr types and quality scores.