The Latency Bottleneck in Edge Vision

Running object detection models like YOLOv8 on edge hardware requires careful memory and frame-rate management. Processing every single frame from a camera stream often saturates the CPU/GPU without adding meaningful diagnostic value—especially when monitoring slow-changing physical environments like plant health or industrial gauges.

In this guide, we will implement a frame-sampling and asynchronous inference wrapper in Python to keep latency low and resource usage predictable.

Designing a Non-Blocking Inference Queue

Instead of running model inference directly inside the camera capture loop, we decouple frame capture from model execution using a bounded queue.Queue of size 1 . This ensures the model always evaluates the most recent frame while dropping stale frames automatically.

import threading import queue import time

class EdgeInferencePipeline : def __init__ ( self , model , target_fps : float = 2.0 ) : self . model = model self .…