Deep Learning Cameras That Run Your Own Models

Deep Learning Cameras AI Vision Solution

A deep learning camera runs a neural network on the camera itself instead of fixed rules. Rule-based vision checks edges, pixel counts, and thresholds that someone sets by hand, which works until parts vary, lighting shifts, or defects stop following a pattern. A model trained on real examples of good and bad parts learns what each defect looks like, so it handles that variety. That is why automated defect detection and other inspection jobs have moved to deep learning.

Roboflow AI1 (opens in a new tab) runs deep learning models on an NVIDIA Jetson Orin NX inside an 8 MP industrial camera (3840 x 2160 at 30 fps) with an integrated 8-LED ring light. It runs object detection and segmentation models such as RF-DETR and YOLO on the camera, with an edge round-trip under 100 ms. When a decision needs more judgment, AI1 can hand that step to a vision-language model in the cloud.

• Featured: Roboflow AI1 (AI1-8MP-4MM): 8 MP sensor, 4 mm lens with electronic focus, NVIDIA Jetson Orin NX 8 GB, IP54 housing
• Attributes: Open model choice, detection and segmentation on-device, 256 GB local storage, over-the-air model and security updates
• Integration: Results to your PLC over OPC UA, MQTT, Modbus TCP, EtherNet/IP, or Profinet, a web HMI for operators, and events to MES, Power BI, or Slack

Your team owns the model and the data. Label images from your own line in Roboflow, train a model, and deploy it to every AI1 from Roboflow Deployment Manager. When a new part or defect appears, add examples, retrain, and push the update over the air without touching the hardware.

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Already running rule-based vision? Roboflow Inference also runs deep learning models on GigE Vision and RTSP streams from the cameras you already have, and alongside existing systems, so you can add deep learning to existing stations with Roboflow, and put AI1 where you need a new camera.