Real-Time Edge Computer Vision: Combining YOLOv10 and WebSockets for Smart Retail & Security

Computer Vision applications require processing dense video frames at high framerates. Sending full 1080p video streams to cloud APIs introduces massive network bandwidth costs.

By running YOLOv10 on edge hardware and transmitting lightweight bounding box metadata over WebSockets, real-time vision applications achieve 60 FPS processing.

1. Real-Time Vision Pipeline

[ IP Camera Stream ] ---> [ Local CUDA Inference (YOLOv10 + OpenCV) ]
                                      |
                                      | Bounding Box Coordinates & Class IDs
                                      v
                             [ WebSocket Gateway ]
                                      |
                                      v
                            [ Live Admin Dashboard ]

2. OpenCV & YOLO Detection Loop

import cv2
from ultralytics import YOLO

model = YOLO("yolov10n.pt")
cap = cv2.VideoCapture(0)

while cap.isOpened():
    ret, frame = cap.read()
    if not ret: break
    
    results = model.predict(frame, stream=True, conf=0.5)
    for r in results:
        boxes = r.boxes.xyxy.cpu().numpy()
        # Broadcast metadata over WebSocket
        websocket_server.broadcast({"boxes": boxes.tolist()})

Edge computer vision delivers real-time spatial intelligence for smart security, inventory counting, and automation.