Vehicle Image Annotation for Insurance Claims, Repair Estimation & Fleet Inspection AI — Case Study
How Shaip delivered specialized vehicle damage annotation combining polygon segmentation of every body part with 11-class damage classification and component-level repair recommendations — built as a production-grade dataset for automated insurance claim processing and vehicle inspection AI.
Project Overview
As insurance and automotive AI moves toward automated claim assessment and vehicle inspection, the client needed an annotation pipeline capable of segmenting every body part of a vehicle alongside detailed damage classification — at a level of granularity that supports component-level repair recommendations.
Shaip built the end-to-end annotation pipeline covering body part polygon segmentation, 11-class damage classification, severity-location-repair attribute tagging, and multi-layer damage intelligence — producing model-ready datasets for automated vehicle inspection and insurance claim AI.
Key Stats
Body Part Coverage
Full Exterior
Damage Classes
11
Attribute Layers
4
Repair Tags
4 Types
Challenges
- Segmenting every body part — bumpers, doors, lights, fenders, alloy wheels, mirrors, pillars
- Classifying 11 distinct damage types — scratch, dent, crack, shatter, paint peel, rust, missing part, broken glass
- Distinguishing subtle damage from surface reflections, dirt, and shadows
- Handling overlapping damage types on a single panel with multiple tags
- Generating component-level repair recommendations — polish, repaint, panel beat, replace
Solution
Body Part Polygon Segmentation
Every visible body part of each vehicle was individually annotated using precise polygon segmentation or bounding boxes, covering the complete exterior structure — front and rear bumpers, hood, trunk, roof, front and rear windshields, side windows, left and right doors, fenders, side mirrors, headlights, tail lights, grille, pillars, side skirts, wheel arches, and alloy wheels.
11-Class Damage Classification
Each annotated body part was tagged with a detailed damage classification covering no damage, light scratch, deep scratch, dent, crack, shatter, paint peel, rust, deformation, missing part, and broken glass. This granular damage taxonomy supports component-level repair triage downstream.
Multi-Layer Damage Intelligence
Additional contextual attributes were applied to each annotation covering damage severity (minor, moderate, severe), damage location on the part (top, center, bottom, left edge, right edge), repair recommendation (polish, repaint, panel beat, replace), and overall part condition score. This multi-layered tagging enables the AI model to generate component-level repair recommendations directly from image data.
Subtle Damage Differentiation
Annotators followed strict visual guidelines to differentiate between scratch types, dent depths, and crack patterns across different vehicle surface materials including metal panels, plastic bumpers, and glass components. Light scratches and paint scuffs were carefully distinguished from normal surface reflections or dirt marks.
Edge Cases & Multi-Damage Panels
Edge cases included damage on curved surfaces requiring high-precision polygon tracing, heavily damaged vehicles with multiple overlapping damage types on the same panel, old versus fresh damage distinguished through surface oxidation and paint condition cues, and damage visible in reflections or at extreme camera angles. Outdoor imagery added complexity through shadow interference and glare on metallic surfaces.
Project Scope
| Dataset Type | Coverage | Damage Classes | Severity Levels | Repair Tags | Methods |
|---|---|---|---|---|---|
| Vehicle damage + body part | Full exterior | 11 classes | 3 (minor / moderate / severe) | 4 (polish / repaint / panel beat / replace) | Polygon + box |
Outcomes
- Established a full-exterior body part segmentation pipeline with damage classification
- Standardized 11-class damage taxonomy spanning scratch to missing part
- Delivered 4-layer damage intelligence (severity, location, repair recommendation, condition score)
- Implemented subtle damage differentiation distinguishing damage from reflections and dirt
- Enabled the client’s insurance claim automation, repair estimation, fleet inspection, and used car valuation AI
Overall, Shaip helped transform a complex vehicle damage annotation requirement into a structured, production-ready pipeline — one capable of supporting automated insurance claim assessment, repair cost estimation, fleet inspection automation, and used car valuation AI with component-level repair intelligence.
Shaip’s vehicle damage pipeline delivered exactly what our claim automation needed — every body part segmented, every damage type classified, every repair recommendation tagged. The granularity translated directly into faster, more accurate claim decisions.
– Director, Claims AI
