Real-Time Embedded AI & Vision Processing Platform
Edge AI system combining Coral TPU, Raspberry Pi, and multi-camera input pipelines for real-time inference and embedded vision processing.
The project demonstrates deployment of lightweight AI models at the edge with hardware-accelerated inference and real-time data acquisition.
This setup reflects modern embedded intelligence systems used in automotive perception, industrial inspection, and IoT edge computing applications.
Technical Overview
The system is built around a heterogeneous edge computing architecture combining CPU-based processing (Raspberry Pi) with hardware-accelerated inference (Coral TPU).
Multi-camera input pipelines are synchronized to enable real-time image acquisition, preprocessing, and inference execution.
The architecture supports low-latency vision processing workflows, including frame acquisition, tensor conversion, model inference, and result streaming.
It demonstrates embedded AI deployment constraints such as memory optimization, inference timing control, and deterministic pipeline execution.
The design reflects how edge AI systems are integrated into automotive and industrial environments where real-time decision-making and resource efficiency are critical.
What This Demonstrates
- Edge AI inference deployment (Coral TPU acceleration)
- Multi-camera real-time vision pipeline
- Raspberry Pi embedded processing architecture
- Hardware-accelerated AI execution
- Real-time data acquisition and inference workflow
- Embedded system optimization for low-latency AI
- Automotive/industrial edge intelligence application patterns