Detailed analysis of model architecture and hyperparameter contributions to performance
Evaluation of different backbone architectures on object detection performance. Comparing CSPDarknet, ResNet, and EfficientNet backbones.
| Backbone | mAP@0.5 | FPS | Params |
|---|---|---|---|
| CSPDarknet53 | 44.3 | 42.2 | 11.2M |
| ResNet50 | 41.8 | 45.1 | 25.6M |
| EfficientNet-B3 | 43.9 | 38.7 | 12.0M |
Analysis of Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) configurations on multi-scale feature fusion.
| Neck Type | mAP@0.5 | Small Objects | Large Objects |
|---|---|---|---|
| FPN Only | 42.1 | 28.3 | 58.7 |
| PAN Only | 43.5 | 31.2 | 59.8 |
| FPN + PAN | 44.3 | 33.1 | 60.2 |
Comparison of detection head designs including anchor-based vs anchor-free approaches and different loss function combinations.
| Head Type | mAP@0.5 | Training Time | Complexity |
|---|---|---|---|
| Anchor-based | 43.2 | 12h | High |
| Anchor-free | 44.3 | 8h | Low |
| Hybrid | 44.1 | 10h | Medium |
Comparison of different edge detection algorithms for lane boundary identification, including Canny, Sobel, and Laplacian operators.
| Method | Accuracy | Processing Time | Robustness |
|---|---|---|---|
| Canny Edge | 85.2% | 15.3ms | 6.2/10 |
| Sobel Edge | 82.7% | 12.1ms | 5.8/10 |
| Laplacian | 79.3% | 8.9ms | 5.2/10 |
Analysis of Hough transform parameter sensitivity including rho and theta resolution, minimum line length, and maximum gap threshold.
| Parameter Set | Accuracy | False Positives | Processing Time |
|---|---|---|---|
| Standard | 85.2% | 12.3% | 15.3ms |
| Fine Resolution | 87.1% | 15.7% | 28.9ms |
| Coarse Resolution | 82.4% | 8.9% | 9.7ms |
Evaluation of geometric transformations including rotation, scaling, translation, and perspective changes on model robustness.
| Augmentation | mAP Improvement | Training Time | Effect |
|---|---|---|---|
| Rotation (±15°) | +2.3% | +15% | Positive |
| Scale (0.8-1.2) | +1.8% | +12% | Positive |
| Perspective | +3.1% | +20% | Positive |
Analysis of color space transformations including brightness, contrast, saturation, and hue adjustments for lighting robustness.
| Augmentation | mAP Improvement | Low-light Gain | Effect |
|---|---|---|---|
| Brightness (±20%) | +1.2% | +4.5% | Positive |
| Contrast (±15%) | +0.9% | +3.2% | Positive |
| Hue (±10°) | +0.3% | +0.8% | Neutral |
Comparison of different classification loss functions including Cross-Entropy, Focal Loss, and Label Smoothing on imbalanced datasets.
| Loss Function | Overall mAP | Rare Classes | Common Classes |
|---|---|---|---|
| Cross-Entropy | 44.3% | 28.7% | 52.1% |
| Focal Loss | 45.8% | 35.2% | 51.9% |
| Label Smoothing | 44.7% | 31.4% | 51.8% |
Evaluation of bounding box regression losses including IoU-based losses (GIoU, DIoU, CIoU) for improved localization accuracy.
| Loss Type | Localization mAP | IoU Threshold | Convergence |
|---|---|---|---|
| L1 Loss | 42.1% | 0.65 | Slow |
| GIoU Loss | 44.3% | 0.72 | Fast |
| CIoU Loss | 45.1% | 0.75 | Fast |
Analysis of learning rate sensitivity on model convergence and final performance. Testing different learning rate schedules and warmup strategies.
| Learning Rate | Final mAP | Convergence Epoch | Stability |
|---|---|---|---|
| 1e-4 | 43.2% | 120 | High |
| 1e-3 | 44.3% | 85 | Medium |
| 1e-2 | 41.8% | 45 | Low |
Evaluation of batch size effects on training stability, memory usage, and final model performance across different hardware configurations.
| Batch Size | mAP | Memory (GB) | Training Time |
|---|---|---|---|
| 8 | 43.8% | 4.2 | Long |
| 16 | 44.3% | 8.1 | Medium |
| 32 | 44.1% | 15.7 | Short |