Accepted inputs
- Inspection-lane images
- Mobile-capture photographs
- Vehicle walkaround video
- Pre-annotations from detection models
AUTOMOTIVE COMPUTER VISION
Automotive and Vehicle-Damage Annotation
Vehicle-damage datasets require consistent decisions about component boundaries, reflections, dirt, pre-existing defects and barely visible marks. We align those rules before scale and choose geometry appropriate to the model objective—from detection boxes to detailed damage masks.
SCOPE
What this service covers
Vehicle and component detectionPanel and part classificationDamage bounding boxesDamage polygons and masksScratch and rim annotationTop, side and underbody views
Annotation approach
- Part and damage taxonomy mapping
- Detection-versus-segmentation selection
- Visibility and severity attributes
- Reflection and contamination rules
- Cross-view consistency checks
Common edge cases
- Faint scratches and low-contrast dents
- Reflections mistaken for damage
- Damage crossing panel boundaries
- Occluded or cropped components
- Dirty, wet or poorly lit surfaces
QUALITY ASSURANCE
Review that follows the guideline
Quality checks are selected for the data type and acceptance criteria. Metrics and review coverage depend on the project, platform and agreed workflow.
Panel-class and damage-type verificationFalse-positive reflection checksBoundary and coverage reviewCross-view consistencyOutput and image-reference validation
Applications
- Drive-through inspection
- Dealership intake
- Rental and fleet condition capture
- Insurance assessment
- Repair-estimation datasets
Output formats
- COCO JSON
- YOLO TXT
- CVAT XML/JSON
- PNG masks
- Client-defined damage schemas
What to include in your brief
- Vehicle-part and damage taxonomy
- Reference examples
- Visibility and minimum-damage thresholds
- Geometry required by model task
- Camera views, output format and acceptance criteria
START WITH A CALIBRATION BATCH
Reduce uncertainty before production.
Share your guidelines, sample characteristics, volume, preferred platform and timeline. We’ll assess the scope and propose a representative calibration step.
Discuss your dataset