• 22 min read
Production Line Monitoring With Camera AI
Build production line monitoring with camera AI: train RF-DETR to detect defects, count bottles, and trigger alerts with Roboflow Workflows.
• 8 min read
Hog Ring Detection with Computer Vision
Detect visible hog rings with RF-DETR, compare their positions with predefined attachment zones, and use Gemini 2.5 Pro to generate an automotive seat inspection summary.
• 10 min read
Flanges Quality Inspection with Computer Vision
Train RF-DETR to catch flange face defects, then sort results into pass, review, and fail.
• 11 min read
Cosmetic Defect Detection with Computer Vision
Learn how computer vision can automate cosmetic defect inspection by detecting paint scratches and identifying the affected car part. This guide shows how to train an RF-DETR model in Roboflow, build a part-level inspection Workflow, and return visual results and repair-ready outputs for QA review.
• 4 min read
How to Scale a Computer Vision Pilot to Production in Manufacturing
See how to scale a computer vision pilot to production: why 77% stall on integration, not the model, and how to get past it in manufacturing.
• 13 min read
Automate Surface Defects Detection with Vision AI
Learn how computer vision can detect surface defects. This guide explains common use cases and shows how to build a Roboflow surface inspection workflow that detects defects, visualizes results, and supports quality decisions.
• 11 min read
How to Detect Paint Defects with Computer Vision
Automate paint defect inspection in manufacturing. Learn how to train an RF-DETR model and build a visual QA pipeline using Roboflow Workflows.
• 4 min read
Making Visual AI Standard Practice in Complex Manufacturing
Learn how to implement visual AI in manufacturing from Florida Crystals' quality lead: trust tests, feedback loops, and pilots that scale.
• 12 min read
Detect Anything Model
A detect anything model finds any object from a text prompt, no training or labels. Build one with SAM3 in Roboflow Workflows to search a scene.
• 15 min read
Defect Inspection
Automate manufacturing defect inspection with computer vision. Learn how to train an RF-DETR model and build a pass/fail QA workflow using Roboflow and Gemini.
• 6 min read
The Vision AI Maturity Model: A Framework for Taking Pilots to Production Scale
Discover a five-level model showing how vision AI evolves from isolated pilots into a reusable system across sites.
• 3 min read
Roboflow and Standard Bots Partner to Bring Custom Visual Intelligence to Every Robot
Roboflow and Standard Bots are announcing a partnership to enable robots to see, understand, and act with visual intelligence.
• 8 min read
Building an AI-Powered Robotic Welding Defect Detection System
Use RF-DETR and Gemini 2.5 Pro to identify welding defects and generate automated quality inspection reports.
• 6 min read
What Is an eQMS in Manufacturing?
Discover what an eQMS is and how integrating computer vision automates manufacturing quality control, from real-time defect detection to CAPA tracking.
• 7 min read
REST APIs in Manufacturing
Learn how REST APIs connect vision AI, MES, and quality systems on the plant floor. Understand when to use REST versus MQTT and OPC UA for industrial data.
• 5 min read
What Goes in a Vision AI Solution Catalog
Scale your enterprise computer vision with a Standard Solution Catalog. Learn the six artifacts needed to move from isolated pilots to global deployments.
• 6 min read
MQTT in Manufacturing with Vision AI
Discover how MQTT connects computer vision models to PLCs, SCADA, and MES systems on the plant floor. Learn to automate defect detection and safety alerts.
• 7 min read
Turn Vision AI Detections Into Alerts: PLC, MES, Slack
Learn how to detect defects with Roboflow and trigger real-time downstream alerts in manufacturing facilities.
• 4 min read
How to Build a Vision AI Center of Excellence
Build a vision AI Center of Excellence in three steps: define a North Star metric, pilot your easiest use case, codify the first catalog entry.
• 5 min read
Who Runs a Vision AI Program: The Builder, Scaler, Operator Model
A vision AI team needs three roles: Builder, Scaler, and Operator. Here is what each one owns and how to staff them as your program scales.
• 5 min read
Why Top-Down Vision AI Rollouts Fail: The Push Trap vs. the Pull Model
Top-down vision AI rollouts stall because plants reject what they didn't specify. Here's why the Push Trap fails and how the Pull Model scales.
• 6 min read
The Computer Vision Manufacturing Integration Stack
Connect computer vision to your manufacturing stack: edge inference plus PLC, SCADA, MES, and ERP integration over EtherNet/IP, MQTT, and OPC UA.
• 7 min read
What Is OPC UA? How Factories Connect Vision AI to PLCs
OPC UA (IEC 62541) explained: what it is, how it compares to MQTT and Modbus, and how it connects Vision AI inspection results to PLCs and MES.
• 7 min read
Is Your Vision AI Program in Pilot Purgatory? A 10-Question Diagnostic
Stuck running vision AI pilots that never scale? Answer these 10 yes/no questions to score whether your program is in pilot purgatory or on the curve.
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