01_OVERVIEW
The Neural Vision Node was developed to solve the latency issues associated with cloud-based computer vision for industrial sorting lines. By shifting inference to the edge, we achieved a 94% reduction in decision-making latency.
This project involved building a custom Debian-based kernel optimized for the Broadcom BCM2711 SoC, ensuring that system interrupts favored the camera serial interface (CSI) during peak processing windows.
02_ARCHITECTURE
Ingestion_Layer
High-speed capture via V4L2 drivers with custom buffer management to prevent frame dropping at 60FPS.
Inference_Engine
TensorFlow Lite models quantized to INT8 format running on the Edge TPU via USB 3.0.
03_TECHNICAL_SPECS
| Parameter | Value | Metric |
|---|---|---|
| Inference Time | 12.4 | ms / frame |
| Power Consumption | 4.2 | Watts (Peak) |
| Operating Temp | 45 - 65 | °C |
04_IMPLEMENTATION
Example snippet of the camera polling loop with custom priority handling:
def start_capture_stream(node_id):
"""
Initializes high-priority capture thread
with custom MMAP buffer allocation.
"""
stream = cv2.VideoCapture(0)
stream.set(cv2.CAP_PROP_FPS, 60)
while True:
ret, frame = stream.read()
if not ret:
log_error(f"NODE_{node_id}: FAIL_SIG")
break
# Priority inference queue
process_edge_frame(frame)