This project demonstrates the use of YOLOv3 (You Only Look Once version 3) for real-time object detection in video streams. YOLOv3 is a state-of-the-art deep learning model known for its balance between speed and accuracy in detecting objects within images and videos.
- Real-Time Detection: Processes video frames quickly enough for real-time applications.
- Multi-Class Detection: Detects multiple objects from a predefined set of classes (e.g., people, vehicles, animals).
- High Accuracy: Provides accurate object localization and classification in each frame.
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Load the Model: The YOLOv3 model is loaded using pre-trained weights and configuration files. These files define the architecture and parameters of the model.
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Prepare Video Stream: Captures frames from a video source (e.g., webcam, video file) for processing.
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Preprocess the Frame: Each frame is resized and normalized to fit the input requirements of the YOLOv3 model.
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Run Detection: The preprocessed frame is passed through the YOLOv3 network to obtain bounding boxes, class labels, and confidence scores for detected objects.
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Postprocess and Display: Detected objects are highlighted with bounding boxes and labels, and the processed frame is displayed in a window to show real-time results.
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Handle Output: Continuously processes video frames and updates the display until the user stops the stream.
- Python: Programming language used for the project.
- OpenCV: Library for video capture, image processing, and display.
- YOLOv3 Model Files:
This project demonstrates how YOLOv3 can be integrated into real-time systems for various applications, such as surveillance, autonomous vehicles, and interactive video analysis.