Build AI Agents for Visual Inspection in Manufacturing

Roboflow’s manufacturing visual-inspection example combines RF-DETR defect detection, ByteTrack object tracking, Gemini-based visual reasoning, and workflow automation to identify potentially damaged cartons and prepare them for human review. The system uses three workflows: OCR reads a station label from the first video frame, a monitoring workflow detects cartons and damage, tracks packages through an inspection zone, saves image evidence, and issues MQTT notifications for flagged packages, while a triage workflow asks Gemini to classify visible damage, estimate severity, suggest a likely cause, and recommend an operator action. A Python runner coordinates the workflows, preserves original and annotated images, prevents duplicate triage events, submits high-severity cases to a webhook, and records outcomes in a JSON report. The approach emphasizes that model assessments, MQTT acknowledgements, and webhook responses do not independently confirm real-world action or correctness, so it recommends evaluating detection, tracking, reasoning, evidence accuracy, escalation performance, and delivery separately while retaining human review for uncertain or invalid cases.

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