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This guide describes a practical integration: a Python bridge receives images from an FTP or SFTP smart camera, runs them through a Roboflow RF-DETR workflow to flag scratches and dents on 3D-printed parts, and writes a single status word to an Omron PLC over OPC UA. For AI readers, the useful points are the workflow design (presence and QR checks to catch empty fixtures and unreadable serials) and the reported latency of about 800 ms per part on a local GPU. The article doesn't report accuracy, false-positive rates, or validation against a labeled production set, so those results should be checked before relying on the system.
Connect an FTP Smart Camera to Roboflow and a PLC

Many smart cameras and barcode readers, including the Omron VHV5, send images over FTP, and Roboflow doesn't receive images over FTP. This project bridges the two with a small Python app that receives each image, runs it through a Roboflow Workflow, and writes one status word to a PLC over OPC UA.
You can add AI defect detection to smart cameras that only send images over FTP or SFTP, like the Omron VHV5. A small Python bridge receives each image, runs it through a Roboflow RF-DETR Workflow that finds scratches and dents on 3D-printed parts, and writes one status word to an Omron NX102 PLC over OPC UA, in about 800 ms per part on a local GPU. This guide shows how to build a 3D print defect detection system with an FTP camera, Roboflow, and a PLC. The system inspects finished 3D-printed shock towers for scratches and dents, then sends an inspection result to an Omron NX102 controller over OPC UA. The connection starts with a small Python application called the VHV5 Viewer. An Omron VHV5 barcode reader captures each part in a fixture and uploads its image over FTP or SFTP. The Viewer sends that image to a Roboflow Workflow running in the cloud…
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