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To integrate OpenCV with a deep learning model for real-time object detection, a pre-trained model like YOLO, SSD, or Faster R-CNN can be loaded using frameworks such as TensorFlow, PyTorch, or OpenCVβs dnn module. OpenCVβs VideoCapture streams video frames, which are preprocessed to match the modelβs input format before running inference to detect objects. Detected objects are visualized using OpenCVβs drawing functions. For optimization, GPU acceleration (e.g., CUDA), model quantization, pruning, and using lightweight models like YOLOv5 or TensorRT can significantly enhance speed and efficiency while maintaining accuracy for real-time performance.
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