Start with imaging conditions
Camera resolution alone is insufficient. Lens, working distance, lighting, line speed and vibration are assessed together. Inspection scope is defined using real product samples and acceptable error categories.
Algorithms and data requirements
Classical image analysis or YOLO-based detection may be appropriate depending on the problem. Training, validation and test data must be separate. False acceptance and false rejection are measured for the production scenario; accuracy percentages are not promised without data.
- Object presence, location and classification
- Production-line inspection scenarios
- Sample data, labelling and acceptance criteria
- PLC decision and reject-mechanism interfaces
Edge AI and field integration
Jetson-based systems or industrial PCs are assessed for model workload, latency and environmental conditions. Local inference and controlled data transfer are project options. This page does not claim an already released camera product or a verified installation.
Technical questions
What is needed for an initial assessment?
Provide sample products, defect categories, line speed, imaging conditions and acceptance criteria. Agree confidentiality requirements before sharing production data.