THE RESEARCH STORY
Begin with the decision—not the model.
A motor claim moves through image collection, component inspection, severity assessment and repair-cost review. Much of this work depends on human inspection and can slow down under high volume. This project studies where computer vision can organize preliminary evidence and improve consistency without replacing accountable review.
Faster claims should not mean careless claims. A useful system reduces repetitive work, structures the evidence and exposes uncertainty so reviewers can focus attention on cases that genuinely require judgement.
PRIMARY RESEARCH QUESTION
What must the study resolve?
How reliably can a model locate visible damage, identify affected vehicle regions and estimate preliminary severity from images captured under real operating conditions?
OBJECTIVES
- 01
Develop image-based detection and classification of visible exterior damage
- 02
Study preliminary severity assessment that remains traceable to visual evidence
- 03
Design a prototype that prioritizes review and escalates uncertain cases
METHOD ARCHITECTURE
From input to accountable support
The method is organized as an evidence chain. Every transformation should preserve enough context to explain what entered the system, what the model inferred, how confidence was assessed and when a person must review the case.
- 01
Image intake and quality checks
- 02
Vehicle and exterior-part detection
- 03
Damage localization and classification
- 04
Preliminary severity estimation
- 05
Evidence package for human review
WORK PACKAGES
Four connected research responsibilities
Data
Design image collection across viewpoints, lighting, vehicle types and damage severity with traceable annotation rules.
Models
Compare detection, segmentation and classification without relying on unrealistically clean test data.
System
Connect the prototype to mobile or retrospective image workflows and preserve reviewable evidence.
Evaluation
Measure accuracy, time, reviewer consistency and cases that must be escalated to specialists.
EVIDENCE PLAN
What the project should produce
A research project is stronger when its outputs can be inspected independently of the final model score. The dossier therefore separates artifacts from evaluation.
Expected research assets
- Prototype for external vehicle-damage detection and classification
- Traceable image-collection and annotation protocol
- Evaluation suite covering accuracy, speed and uncertainty
- Escalation workflow for complex cases requiring reviewers
- Research assets for publication, IP or industry co-development
Evaluation dimensions
- Detection and segmentation quality
- Severity agreement with reviewers
- Calibration and abstention quality
- Inference and end-to-end review time
- Performance across lighting, viewpoints and vehicle groups
RESEARCH TRANSLATION
From prototype to a credible working system
Potential users include insurers, repair networks, service centres, inspectors, claim-platform developers and fleet operators. Operational integration still requires defined data access, image standards, audit trails and accountable decision ownership.
LIMITATIONS AND RESPONSIBLE FRAMING
The boundary is part of the research design.
This is a preliminary decision-support system, not an autonomous claim approver. Images may not reveal structural damage, and repair valuation also requires parts, labour, policy conditions and accountable human inspection.
This dossier communicates the documented project design and intended evaluation. It does not convert a proposal, funded status or prototype into a claim of validated operational performance. Numerical results should be cited only from a resulting peer-reviewed publication or verified project report.
PROJECT RECORD AND CITATION
A stable record for this research direction
FORMAL PROJECT TITLE
Car Damage Estimation System using Deep Convolutional Neural NetworkSuggested citation for this public dossier:
Arayaphan, W. (2023). Car Damage Estimation System using Deep Convolutional Neural Network. Research project dossier, Warin Arayaphan.Use the original project report or resulting publication when citing methods, data or findings.STUDENT PATHWAYS
Where students can enter the research
Student directions include data annotation, domain shift, damage segmentation, uncertainty calibration, mobile inference, explainability and workflow analytics.
We can shape it into a dataset, experiment, model, system component or responsible deployment study.