Steel Strip Defect Inspection

Automating steel surface defect inspection is achieved by training an RF-DETR Small model and integrating it into a Roboflow Workflow that categorizes strips into pass, review, or fail, moving beyond a simple pass-fail evaluation. Trained inspectors often miss 30 to 40 percent of defects, which can lead to quality issues downstream in processes like stamping or coating. By using a public dataset annotated for six defect classes, the system trains a model that achieves significant accuracy in defect detection. The Workflow involves steps to visualize bounding boxes, sort defects based on confidence levels, and log inspection events, ensuring a comprehensive quality check for each strip. The model's adaptability allows for continuous improvement as it learns from human-reviewed cases, thereby increasingly supporting inspectors rather than replacing them. This approach not only improves defect detection but also ensures a robust inspection system adaptable to evolving defect classes and conditions.