Powder-Coat Defect Detection
Blog post from Roboflow
A Roboflow workflow for powder-coat inspection uses an RF-DETR Small object-detection model to identify and localize craters, orange peel, paint bubbles, and scratches, then uses Gemini 2.5 Pro to generate a short description of defect type, location, apparent severity, and need for human review. Trained on a focused subset of 2,775 annotated images using a 70/15/15 data split and 384×384 preprocessing, the model achieved 74.0% mAP@50, 72.2% precision, and 77.1% recall, though its performance should be validated under actual production conditions such as varying lighting, gloss, angles, and surface colors. The workflow annotates images with detection boxes and labels, overlays Gemini’s constrained one-sentence summary, and logs original images, predictions, annotations, and summaries through Roboflow Vision Events for later quality analysis. The system can be expanded for continuous camera-based inspections, confidence thresholds, defect counting, notifications, and review routing, but it is intended as an inspection aid because visual detections and severity estimates require human verification rather than serving as final quality decisions.
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