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Vehicle Damage Instance Segmentation
Model Description
- Description: This YOLOv8-seg model is designed to automate vehicle insurance claims by isolating damage areas (Dents, Scratches, Broken Glass) with pixel-level accuracy.
- Training Approach: Fine-tuned from a YOLOv8-seg foundation model using the Ultralytics framework.
- Intended Use Case: Mobile app integration to allow claimants to get immediate repair estimates, significantly reducing manual inspection wait times.
Training Data
- Source: Roboflow Universe.
- Volume: 10,218 total images post-augmentation.
- Classes: Dents, Scratches, and Broken Glass.
- Annotation Process (Original Work): I performed a manual audit of roughly 8 hours, refining approximately 15% of the polygon masks to ensure tighter boundaries for precise surface area calculations.
- Split: 70% Training, 20% Validation, 10% Testing.
- Augmentation: Mosaic (first 90%), Horizontal Flip, and Scale (+/- 10%).
Training Procedure
- Hardware: Google Colab T4 GPU.
- Optimizer: AdamW | Learning Rate: 0.002.
- Inference Speed: ~3ms per frame.
Evaluation Results
- Overall Metrics:
- mAP50 (Mask): 0.842 (Target was 0.85).
- Precision: 0.864 | Recall: 0.771.
- Key Findings: Broken Glass achieved a near-perfect recall of 0.94 due to high-contrast edges.
- Performance Analysis: Brightness and contrast augmentations during the iteration process improved final detection accuracy by 15%.
Key Visualizations
Confusion Matrix
Shows model performance and identifies a 12% false positive rate for scratches in direct sunlight.
Training Results
Loss curves showing model convergence over the training period.
Visual Examples
Representative ground truth samples showing successfully segmented damage on curved metallic panels.
Limitations and Biases
- Glare: Shiny paint reflections cause a 12% false positive rate for scratches in direct sunlight.
- Scale: Small scratches under 1 inch are often missed.
- Depth: The model provides 2D surface area but lacks 3D dent depth for volume estimation.
- Ethical Consideration: This model is an appraisal tool; it should not be the sole basis for final legal or financial insurance payouts without human review.
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