Machine-Vision AI for Surface Defect Severity Assessment
Blog post from Roboflow
Mostafa Ibrahim describes a Roboflow Workflow for leather quality inspection that extends RF-DETR defect detection into automated severity decisions based on bounding-box area. Using a public annotated leather-defect dataset, the process involves training an RF-DETR model, detecting defects at a 0.4 confidence threshold, and using a Custom Python block to classify images as PASS when no defects are found, REVIEW when only defects below a configurable 9,000-pixel-square area threshold appear, or FAIL when any defect exceeds that threshold. The workflow also overlays boxes, labels, and inspection status on images, produces a structured JSON quality report, and logs original and annotated images, detections, and metadata to Roboflow Vision Events for traceability. Test examples show large cuts and folds failing automatically, a small but confidently detected cut being routed for review, and clean leather passing without manual intervention. The approach can run on edge hardware such as NVIDIA Jetson or through an API, supports monitoring across production lines and suppliers, and can use production failure cases to improve future model training.
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