AI & Computer Vision

Edge AI Deployment: From Cloud to Device in Industrial Applications

The promise of AI in industrial applications often collides with practical constraints: latency requirements, bandwidth limitations, data privacy concerns, and the simple fact that many industrial environments lack reliable internet connectivity. Edge AI — running inference directly on local hardware — addresses all of these challenges.

Why Edge AI?

Consider a quality inspection system on a production line running at 100 parts per minute. Sending each image to a cloud API introduces 100-500ms of latency, requires sustained bandwidth of several Mbps, and creates a dependency on internet availability. Edge AI processes images locally in 10-50ms with zero network dependency.

Key Benefits

• Low latency: 10-50ms inference vs. 100-500ms cloud round-trip.
• Bandwidth savings: Process locally, send only results and alerts.
• Privacy: Sensitive images never leave the premises.
• Reliability: Operates independently of internet connectivity.
• Cost: No per-inference cloud charges at scale.

Hardware Options for Edge AI

Platform	Performance	Power	Best For
NVIDIA Jetson Nano	472 GFLOPS	5-10W	Entry-level, education
NVIDIA Jetson Orin NX	100 TOPS	10-25W	Production deployments
NVIDIA Jetson AGX Orin	275 TOPS	15-60W	Multi-stream, complex models
Intel Neural Compute Stick	~4 TOPS	~1W	USB add-on for existing systems

Real-Time Object Detection with YOLO

YOLO (You Only Look Once) has become the standard for real-time object detection. At Rays Technology, we deploy optimized YOLO models for various industrial applications:

• YOLOv8 Nano: 3.2M parameters, runs at 60+ FPS on Jetson Orin NX. Ideal for real-time monitoring with multiple camera streams.
• YOLOv8 Medium: 25.9M parameters, higher accuracy for critical detection tasks where false negatives are costly.
• Custom trained models: We train application-specific models on your data for optimal detection of domain-specific objects.

Deployment Pipeline

1. Data collection: Gather representative images from the actual deployment environment.
2. Annotation: Label objects of interest with bounding boxes or segmentation masks.
3. Training: Fine-tune pre-trained models on your specific dataset.
4. Optimization: Convert to TensorRT for NVIDIA platforms or OpenVINO for Intel.
5. Integration: Deploy with monitoring, alerting, and reporting capabilities.
6. Maintenance: Continuous monitoring and periodic retraining as conditions evolve.

Integration with Sensor Fusion

At Rays Technology, we integrate edge AI with our broader sensor platforms. Camera-based detection combined with radar ranging, LiDAR 3D mapping, and RF spectrum monitoring creates systems greater than the sum of their parts. Critical infrastructure monitoring is a strong example of this multi-sensor AI approach.

Learn more about our AI solutions →