Flanges Quality Inspection with Computer Vision

The tutorial describes building an automated flange-quality inspection system in Roboflow to detect scratches, cracks, dents, and pinholes before defective parts are installed. It uses a labeled flange dataset from Roboflow Universe to train an RF-DETR Small object-detection model with standard augmentations, then deploys the model in a workflow that translates source labels into English, visualizes detected defects, removes duplicate detections, and assigns PASS, REVIEW, or FAIL outcomes based on confidence thresholds. The workflow produces an annotated image, a structured quality report, and a brief Gemini-generated operator recommendation, while Roboflow Vision Events records inspections with contextual metadata. Examples demonstrate high-confidence defects resulting in failure, lower-confidence detections being routed for manual review, and defect-free images passing automatically. The approach is intended for production use through APIs or edge inference, with reviewed cases added back into training to improve the model and reduce manual inspection over time; Roboflow Agent can alternatively assemble much of the pipeline from a natural-language request.

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