Decoupling Frame Capture From Inference Cuts Edge Vision Latency in Python

The article addresses the problem that running object detection models such as YOLOv8 on every frame of an edge camera stream can overwhelm CPU or GPU resources. For slow-changing monitoring tasks like plant health or industrial gauges, processing each frame adds little diagnostic value. The author proposes a Python pipeline that separates frame capture from model execution using a bounded queue of size one, so the model always evaluates the latest frame and stale frames are discarded. The approach also includes a worker thread that paces inference to a target frame rate and a helper function for logging inference latency to monitor service health. The author argues that this design keeps resource usage predictable and enables local deployment without cloud round-trip delays.