How to Verify Torque Marks with Computer Vision

A Roboflow tutorial describes an automated torque-mark inspection workflow designed to identify whether painted seals on fasteners remain aligned, are misaligned after rotation, or cannot be read reliably. It uses a 517-image labeled dataset to train an RF-DETR instance-segmentation model that distinguishes torque seals from glare and other objects, then merges fragmented detections with a custom Python step. A Gemini vision-language model evaluates each detected seal’s continuity and alignment at the seam between the fastener and its base, while additional workflow components convert its judgment into PASS, FAIL, or UNREADABLE results, annotate images, generate JSON reports, and log inspections through Roboflow Vision Events. The approach is intended to reduce errors from manual visual checks, supports hosted or local deployment with images from cameras or streams, and treats missing or unclear marks conservatively as unreadable unless configured otherwise.

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