Datasets:

cvtechniques
/

vehicle-damage-segmentation

	# 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
	![Confusion Matrix](confusion_matrix.png)
	Shows model performance and identifies a 12% false positive rate for scratches in direct sunlight.

	Training Results
	![Results](results.png)
	Loss curves showing model convergence over the training period.

	## Visual Examples
	![Ground Truth](val_batch0_labels.jpg)
	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.

	# 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
	![Confusion Matrix](confusion_matrix.png)
	Shows model performance and identifies a 12% false positive rate for scratches in direct sunlight.

	Training Results
	![Results](results.png)
	Loss curves showing model convergence over the training period.

	## Visual Examples
	![Ground Truth](val_batch0_labels.jpg)
	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.