"Smart claim" refers to a modern approach to managing insurance claims, often using technology like artificial intelligence (AI) and automation to streamline the process. These systems aim to improve efficiency and accuracy by automating tasks like status checking, fraud detection, and decision-making, which can lead to faster processing, reduced costs, and a better customer experience.

1. EAST WEST INSTITUTE OF TECHNOLOGY
BENGALURU-560091
(Affiliated to Visvesvaraya Technological University, Belgaum, Karnataka)
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
PROJECT PRESENTATION
ON
“SmartClaim: Deep Learning-Driven Framework for Vehicle Damage
Classification and Cost Estimation”
BACHELORE OF ENGINEERING
IN
COMPUTER SCIENCE & ENGINEERING
Under the Guidance of
Dr Usha B S
Professor
Dept. of CSE
Submitted by
AAKARSHANA S G 1EW21CS002
YASHASWINI J N 1EW21CS176
SAHANA S 1EW21CS134
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

2. LIST OF CONTENTS
 INTRODUCTION
 ABSTRACT
 LITERATURE SURVEY
 PROBLEM STATEMENT
 OBJECTIVES
 PROPOSED SYSTEM
 IMPLEMENTATION AND TESTING
 RESULTS
 CONCLUSION
 REFERENCES

3. INTRODUCTION
1. Need.
• Millions of vehicular accidents occur globally, necessitating accurate and timely damage assessment.
• Manual damage inspections are time-consuming, prone to human error, and often influenced by bias.
• Vehicle rental and insurance industries require a reliable, automated system to streamline inspections and
reduce delays.
Fig. Traditional insurance claiming process.
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4. 2. Historical context.
• Conventional inspections involve visual checks by inspectors, causing delays in insurance claims and repair cost
estimations.
• Advances in machine learning and image recognition, particularly Convolutional Neural Networks (CNNs), have
transformed automated damage detection.
• CNN models like VGG16 have proven effective in image classification and visual recognition tasks.
Fig. Evolution of Vehicle Damage Assessment Methods.
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5. 3. Overview.
• This project presents a deep learning-driven framework for vehicle damage classification and cost estimation.
• Uploaded vehicle images are processed to detect damage, classify severity levels, and predict repair costs based on the
vehicle’s specifications.
• The system generates a comprehensive report, enhancing efficiency and accuracy in damage assessment.
Fig. Workflow of Automated Vehicle Damage Assessment System.
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6. ABSTRACT
• This project introduces a web-based system for automated car damage detection and cost estimation.
• It leverages the YOLO object detection model to identify damaged car parts from uploaded images.
• Flask is used to manage user interaction, image processing, and result display.
• Predefined part prices are utilized to calculate repair costs based on detected damages.
• The system ensures accuracy, efficiency, and user-friendliness, reducing manual effort in damage assessment.
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7. TITLE AUTHORS YEAR OBJECTIVES ADVANTAGES DISADVANTAGES
Automated Vehicle
Damage Detection and
Repair Cost Estimation
Using Deep Learning
Sunil Kumar
Aithal S
K Chirag
Nackathaya
Dhanush
Poojary
Gautham
Bhandary
Avinash
Acharya
2024  To develop an automated
system using deep
learning to detect vehicle
damages, estimate repair
costs, and provide a
quick, user-friendly
platform for stakeholders.
 The automated system
enhances efficiency and
reliability by reducing
reliance on manual
inspections, offering
high accuracy in
damage detection
through state-of-the-art
deep learning models,
and streamlining
insurance and repair
workflows.
1. Requires high-quality
images and preprocessing,
with limited generalization
to diverse vehicles and
damages.
2. High computational
demands and reliance on
fixed cost standards may
limit scalability.
A System for Automated
Vehicle Damage
Localization and
Severity Estimation
Using Deep Learning
Yantai Ma
Hiva Ghanbari
Tianyuan Huang
Jeremy Irvin
2024
 To design an automated
system that uses deep
learning for vehicle
damage localization and
severity estimation by
analyzing user-submitted
photographs, generating a
detailed damage report.
 Utilizes deep learning
modules for vehicle
identification, part
localization, and
damage localization,
offering flexible,
scalable solutions
integrated with
customer workflows.
1. Utilizes deep learning
modules for vehicle
identification, part
localization, and damage
localization.
2. Challenges in
generalization to new
vehicle models.
LITERATURE SURVEY
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8. Vehicle Damage
Severity Estimation for
Insurance
Operations Using In-
The-Wild Mobile
Images
Dimitrios
Mallios
Li Xiaofei
Niall Mclaughlin
Jesus Martinez
Del Rincon
Clare Galbraith
Rory Garland
2023
 To develop an
automated system for
vehicle damage
severity estimation and
cost assessment using
multi-angle images and
structured vehicle data.
 Increased
Efficiency:
Automates the
vehicle damage
assessment
process,
significantly
reducing time and
labor costs.
1. Image Quality
Dependency: The
system's accuracy relies
heavily on the quality
and clarity of user-
uploaded images.
2. Limited Context
Understanding: The
model may struggle to
detect internal or non-
visible damages that are
not captured in the
images.
Vehicle-Damage-
Detection
Segmentation
Algorithm Based on
Improved Mask RCNN
Qinghui Zhang
Xianing Chang
Shanfeng Bian
2020  To develop an
improved Mask
RCNN-based vehicle
damage detection
algorithm that
enhances average
precision, detection
accuracy, and masking
accuracy, enabling
efficient and accurate
resolution of traffic
accident compensation
issues.
 Improved
accuracy and
efficiency in
vehicle damage
detection,
enabling faster
resolution of
traffic accident
compensation
through
optimized Mask
RCNN.
1. Limited to detecting
external vehicle
damage, unable to
assess internal issues.
2. Performance may
decrease under
challenging conditions
like poor lighting or
unusual angles.
TITLE AUTHORS YEAR OBJECTIVES ADVANTAGES DISADVANTAGES
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9. Vehicle Damage
Classification and
Fraudulent
Image Detection
Including Moiré Effect
Using
Deep Learning
Umer Waqas
Nimra Akram
Soohwa Kim
Donghun Lee
Jihoon Jeon
2020
 To develop a
lightweight deep
learning-based system
that classifies vehicle
damages into three
categories (medium,
huge, and no damage)
while incorporating a
hybrid approach to
detect and filter
fraudulent images
 Robust detection
of fraudulent
images through
metadata analysis
and Moiré effect
detection,
achieving 99%
accuracy in
ensuring image
authenticity.
1. Limited to external
cosmetic damages, lacking
capability to assess internal
vehicle issues such as engine
or structural alignment.
2. Performance may be
affected by challenging
conditions like poor lighting,
awkward angles
TITLE AUTHORS YEAR OBJECTIVES ADVANTAGES DISADVANTAGES
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10. PROBLEM STATEMENT
To address these issues, this project aims to develop an automated system that utilizes the YOLO object
detection model to identify damaged vehicle parts from uploaded images and estimate repair costs based on
predefined pricing for faster, accurate, and objective evaluations, enhancing efficiency and transparency for
both insurers and policyholders.
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11. OBJECTIVES
The Main objectives of the project are:
• Develop a web-based platform for automated car damage detection and cost estimation.
• Integrate YOLO object detection to accurately identify damaged car parts from images.
• Enable real-time calculation of repair costs based on predefined part prices.
• Provide a user-friendly interface for image upload and displaying results.
• Ensure modular and scalable architecture for future enhancements and customizations.
• Enhance efficiency in damage assessment, reducing manual effort and time.
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12. PROPOSED SYSTEM
1. Architecture/Design.
System Architecture.
The system architecture follows a web-based client-server model:
• Client-Side:
• HTML templates (dashboard.html, estimate.html) for user interaction.
• Users can upload images of damaged cars.
• Server-Side:
• Flask server processes the uploaded image.
• YOLO model is used to detect car parts in the image.
• Damage estimation logic calculates the total cost of repairs based on detections.
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13. Fig. Architecture of system
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14. .
• Data Storage:
• Uploaded and processed images are stored in the static/ directory.
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Fig. Flowchart of architecture

16. 2. Algorithms/Mathematical Models
Algorithm for Damage Estimation.
1. Input: Uploaded image (image_path), predefined part prices (PART_PRICES).
2. Processing:
• Pass the image through the YOLO model to detect objects.
• Extract detected part IDs (class_ids) and their counts (class_counts).
• Match detected IDs to car parts using a predefined class-to-part mapping.
• Multiply detected counts by their respective part prices.
3. Output: Cost estimation for individual parts and total repair cost.
Upload → Detect → Calculate → Display Results.
Fig. Flowchart of algorithm.
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17. Mathematical Model
1. Detection Mapping:
2. Cost calculation:
Where:
n: Number of detected part types.
Count : Number of instances detected for part .
𝑖 𝑖
Price ​
: Price for one instance of part .
𝑖 𝑖
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3. Module-wise Description:
• Flask Application
• Functionality:
• Routes (/, /dashboard) to manage user interaction.
• Renders templates (dashboard.html, estimate.html) for the user interface.
• Key Functions:
• dashboard(): Handles image uploads, processes the image, and calculates estimates.
• get_damage_info(class_counts): Maps detected parts to names and computes cost estimates.
• Image Processing and Detection
• YOLO Integration:
• Loads a pre-trained YOLO model (best.pt).
• Processes uploaded images to detect car parts and their locations.Output:Detected objects with class IDs, bounding
boxes, and counts.
• Output:
• Detected objects with class IDs, bounding boxes, and counts.

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• Damage Estimation
• Predefined Prices: Dictionary PART_PRICES contains prices for each car part.
• Logic:
• Maps detected class IDs to car part names.
• Computes total cost for each part based on the count.
• File Management
• Image Handling:
• Uses secure_filename to handle file uploads securely.
• Saves uploaded and processed images in the static/ directory.
• Error Handling: Ensures only valid image files are uploaded.
• User Interface
• Templates:
• dashboard.html: Upload interface for images.
• estimate.html: Displays detected parts, counts, and cost estimates.
• Flask Flash Messages: Used to notify users of errors (e.g., invalid file type).

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IMPLEMENTATION
1. Dashboard

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2. Image detection.
3. Cost Estimation.

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4. User Interface and Results Display.
Dashboard Template Rendering:
Estimate Template Rendering:

23. TESTING
• Validate the YOLOv8-based car damage detection model for accuracy and efficiency.
• Test Environment:
• Dataset: Car Damage Detection.v2i.yolov8.
• Classes: Bonnet, Bumper, Dickey, Door, Fender, Light, Windshield.
• Training Configuration:
Model: YOLOv8n (pretrained).
Optimizer: Adam with cosine learning rate scheduling.
Device: GPU (if available).
• Performance Metrics:
• mAP50: Measures detection accuracy at a single IoU threshold of 50%.
• mAP50-95: Measures detection accuracy across multiple IoU thresholds.
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USE CASES
Car Damage Detection System:
• Input: Image of a car with visible damages (e.g., damaged bumper and windshield).
• Expected Output: Identification of damaged parts with bounding boxes and cost estimation based on
predefined prices.
• Actual Output: The system accurately detected the damaged bumper and windshield, providing repair costs
consistent with predefined rates.
Vehicle Inspection Assistance:
• Input: Image of a vehicle submitted for routine inspection.
• Expected Output: Detection of damages, if any, and generation of a report for repair suggestions.
• Actual Output: The model identified minor scratches on the fender and estimated repair costs, enabling timely
maintenance.

25. RESULTS
1. Homepage (Dashboard).
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Fig. User Dashboard for Image Upload and Detection.

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2. Warning for Missing File.
Fig. Error Message for Missing Image Upload.

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3. Upload the photo.
Fig. Image Uploaded Successfully for Damage Detection.

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4. Side View Detection.
5. Front View Detection.
Fig. Detected Damages and Cost Estimation for Side View.
Fig. Detected Damages and Cost Estimation for Front View.

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5. Rear View Detection.
Fig. Detected Damages and Cost Estimation for Rear View.

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6. Non-Damaged Car.
Fig. No Damage Detected for Non-Damaged Vehicle.

31. CONCLUSION
This project implements an automated system for detecting car damages and estimating repair costs using a web-
based platform. It integrates Flask for managing user interactions, image uploads, and result display. The YOLO
object detection model efficiently identifies damaged car parts from uploaded images, while a predefined price
structure calculates repair costs. The system is user-friendly, providing real-time cost estimation with visualized
damage results. Its modular architecture allows easy customization and scalability for future enhancements like
database integration and expanded part detection.
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FUTURE ENHANCEMENTS
• Damage Severity Classification – Identify minor, moderate, and severe damages.
• Multi-Angle Image Processing – Support for multiple images from different angles.
• Cloud-Based Deployment – Deploy the system online for public access.
• Real-Time Detection – Support video-based real-time damage assessment.

33.  S. K. Aithal, K. C. Nackathaya, D. Poojary, G. Bhandary, and A. Acharya, "Automated vehicle damage detection
and repair cost estimation using deep learning," in Proc. 2nd Int. Conf. Sustainable Comput. Smart Syst. (ICSCSS),
pp. 1480–1484, 2024. doi: 10.1109/ICSCSS60660.2024.10625107.
 Y. Ma, H. Ghanbari, T. Huang, J. Irvin, O. Brady, S. Zalouk, H. Sheng, A. Ng, R. Rajagopal, and M. Narsude, "A
system for automated vehicle damage localization and severity estimation using deep learning," in Proc. IEEE Int.
Conf. Intell. Transp. Syst. (TITS), pp. 5627–5639, 2024. doi: 10.1109/TITS.2023.3334616.
 D. Mallios, L. Xiaofei, N. McLaughlin, J. M. D. Rincon, C. Galbraith, and R. Garland, "Vehicle damage severity
estimation for insurance operations using in-the-wild mobile images," IEEE Access, vol. 11, pp. 78644–78655,
2023. doi: 10.1109/ACCESS.2023.3299223.
 Q. Zhang, X. Chang, and S. Bian, "Vehicle-damage-detection segmentation algorithm based on improved Mask
RCNN," IEEE Access, vol. 8, pp. 6997–7004, 2020. doi: 10.1109/ACCESS.2020.2964055.
 U. Waqas, D. Lee, N. Akram, J. Jeon, and S. Kim, "Vehicle damage classification and fraudulent image detection
including Moiré effect using deep learning," in Proc. IEEE Canadian Conf. Electr. Comput. Eng. (CCECE), pp. 1–6,
2020. doi: 10.1109/CCECE.2020.123456.
REFERENCES

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THANK YOU