Optimizing Real-Time Computer Vision Inference Pipelines in Python for Edge Monitoring

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.interval = 1.0 / target_fps
        self.frame_queue = queue.Queue(maxsize=1)
        self.running = False

    def push_frame(self, frame):
        if self.frame_queue.full():
            try:
                self.frame_queue.get_nowait()
            except queue.Empty:
                pass
        self.frame_queue.put(frame)

    def _worker(self):
        while self.running:
            start_time = time.perf_counter()
            try:
                frame = self.frame_queue.get(timeout=1.0)
                results = self.model.predict(frame, verbose=False)
                self.handle_detections(results)
            except queue.Empty:
                continue

            elapsed = time.perf_counter() - start_time
            sleep_time = max(0.0, self.interval - elapsed)
            time.sleep(sleep_time)

    def handle_detections(self, results):
        for r in results:
            boxes = r.boxes
            print(f"Detected {len(boxes)} objects at {time.strftime('%X')}")

    def start(self):
        self.running = True
        threading.Thread(target=self._worker, daemon=True).start()

    def stop(self):
        self.running = False

Adding Basic Telemetry Metrics

To monitor how healthy our inference service is in production, we should track average inference duration and frame drop rates:

def measure_inference(model, sample_image):
    t0 = time.perf_counter()
    _ = model.predict(sample_image, verbose=False)
    latency_ms = (time.perf_counter() - t0) * 1000
    print(f"Inference latency: {latency_ms:.2f} ms")
    return latency_ms

Conclusion

Decoupling frame capture from model inference prevents memory leaks and keeps edge hardware responsive. Combined with lightweight latency logging, you can reliably deploy computer vision models on local hardware without cloud round-trip delays.