To build a real-time shuttlecock detection system, you’ll need a combination of hardware and software components optimized for speed and accuracy. Start by selecting a camera system capable of high frame rates (e.g., 120 FPS or higher) to capture fast-moving shuttlecocks, paired with sufficient lighting to ensure clear footage. For software, use a lightweight machine learning model like YOLOv8-S or MobileNet-SSD, which balances speed and precision. These models can be trained on a custom dataset of shuttlecock images under various conditions (e.g., different angles, lighting, and backgrounds) to improve generalization. Tools like LabelImg or Roboflow can help annotate training data efficiently. Preprocessing steps such as resizing frames to 640x640 pixels and normalizing pixel values will streamline inference.
Next, optimize the detection pipeline for real-time performance. Deploy the model using a framework like TensorFlow Lite or ONNX Runtime to reduce latency, and consider hardware acceleration with GPUs (e.g., NVIDIA Jetson devices) or TPUs for edge deployment. To handle occlusion or fast motion, implement a tracking algorithm like a Kalman filter or SORT (Simple Online and Realtime Tracking) to predict the shuttlecock’s trajectory between frames. For example, if the model misses a detection in one frame, the tracker can estimate its position based on prior movement. Additionally, use multi-threading to separate camera input, inference, and result processing, ensuring the system doesn’t bottleneck on a single task.
Finally, validate the system in real-world scenarios. Test under varying conditions—such as indoor vs. outdoor play or different shuttlecock colors—to identify edge cases. Use metrics like FPS (aim for ≥30 FPS), precision, and recall to measure performance. If false positives occur, retrain the model with hard negative mining. For integration, pair the detection output with a notification system (e.g., audio alerts or LED indicators) to provide instant feedback. Open-source tools like OpenCV for video handling and PyTorch for model training can form the backbone of this system, while platforms like Raspberry Pi or Arduino can manage hardware integration. Regularly update the model with new data to maintain accuracy as environmental conditions change.