Visual Defect Detection with Edge AI
Identifying manufacturing defects such as cracks, scratches, missing components, or surface irregularities on production lines using on-device image classification or object detection. Models are trained on good vs defective part images and deployed on MCUs with camera interfaces. Enables real-time quality inspection at the point of manufacture without network dependency or cloud latency.
Hardware Requirements
| Minimum RAM | 128 KB |
| Minimum Flash | 512 KB |
| Sensor Inputs | camera |
| Typical Model Size | 150 KB (quantized int8) |
| Minimum Clock | 80 MHz |
Hardware Guides
No hardware guides for visual defect detection yet. Use the MCU Checker to find compatible hardware.
Industry Applications
Orchestrate Visual Defect Detection with ForestHub
Your devices run visual defect detection on-device. ForestHub on your Linux edge gateway ingests their results over MQTT/Modbus/OPC-UA, orchestrates the sense-reason-act loop as an auditable graph, and acts on the line — the LLM is one node among many.