docs: update detection improvement plan with evaluation and infrastructure details

DETECTION_IMPROVEMENT_PLAN.md

@@ -42,3 +42,22 @@ Prevent identity jumping in live mode:
 | **Phase 2** | **Geometry** | Perspective Warping (Flattening) | Significant boost in classification accuracy. |
 | **Phase 3** | **Intelligence** | Unified Model + Expanded Dataset | Higher precision and lower inference latency. |
 | **Phase 4** | **Architecture** | Full Object Detection Model (YOLO) | Industry-standard reliability and speed. |
+
+## Evaluation & Validation
+To measure the impact of these improvements, the following metrics will be tracked:
+- **Precision & Recall**: Measure the accuracy of card identity (Suit + Value) across diverse lighting environments.
+- **Latency**: Track the time from frame capture to identity assignment to ensure real-time performance (<100ms).
+- **Stability Score**: Percentage of frames where a card's identity remains constant while stationary.
+- **False Positive Rate**: Frequency of "ghost" cards detected in empty table areas.
+
+## Technical Infrastructure
+Implementation will leverage the following tools:
+- **OpenCV.js**: For Canny Edge Detection, Contour Approximation, and Perspective Transforms (Homography).
+- **TensorFlow.js**: For the classification heads and potential YOLO implementation.
+- **Synthetic Dataset Generator**: A script to generate warped and blurred card images to augment the training set without manual labeling.
+
+## Testing Strategy
+- **Baseline Benchmarking**: Create a "Golden Set" of 100 static images with known labels to test every architectural change.
+- **Environmental Stress Tests**: Test under three specific lighting scenarios: Low-light, Direct Overhead Light (shadows), and Natural Side Light.
+- **Integration Testing**: Verify that the Perspective Correction doesn't introduce latency that disrupts the Temporal Smoothing window.
+