Call For Paper - Upcoming Conferences
Research Article | Open Access | Download PDF
Volume 13 | Issue 4 | Year 2026 | Article Id. IJME-V13I4P111 | DOI : https://doi.org/10.14445/23488360/IJME-V13I4P111

Evaluation of Mechanical Damage Characterization, Automotive Panel Deformation and Repair Cost Estimation using Deep Learning Techniques

N.V. Sailaja, CH V K N S N Moorthy, Singam Vamshi Krishna, T Varshitha, Shaik Mahimood Pasha, Jangamgari Sandeep Kumar

Received Revised Accepted Published
21 Jan 2026 28 Feb 2026 30 Mar 2026 29 Apr 2026

Citation :

N.V. Sailaja, CH V K N S N Moorthy, Singam Vamshi Krishna, T Varshitha, Shaik Mahimood Pasha, Jangamgari Sandeep Kumar, "Evaluation of Mechanical Damage Characterization, Automotive Panel Deformation and Repair Cost Estimation using Deep Learning Techniques," International Journal of Mechanical Engineering, vol. 13, no. 4, pp. 132-146, 2026. Crossref, https://doi.org/10.14445/23488360/IJME-V13I4P111

Abstract

In the automobile insurance sector, there is a significant requirement to improve the efficiency and accuracy of the process of damage analysis following vehicle accidents. Conventional approaches require the manual inspection of the damaged areas of the vehicle, which is time-consuming and susceptible to human error. These factors may lead to delays in the processing of the claim and the evaluation of the damage. There are no self-service facilities where policyholders can upload images and videos to aid in the process of the insurance claim. To improve upon these disadvantages of the traditional process, AI-based computer vision systems are highly appropriate and can efficiently use video and image analysis techniques to improve the accuracy of the process of damage analysis. These systems accurately measure the degree of damage, which accelerates the process of claims and minimizes the reliance on human verifications. AI-based damage detection includes estimation of the cost of repair and handling of claims and enhances the overall policyholder experience. One of the significant innovations of the AI-based system is the inclusion of deep learning methodologies to improve the accuracy of the process of estimating the costs of repairs of the damaged areas of the motor vehicle. These systems analyze damage data with higher precision, providing insurers with authoritative estimates that enable equitable and timely payment of claims. The proposed work aims to allow insurers to make decisions on whether to repair or replace vehicle parts depending on vehicle type and other variables. In addition, the development of self-service tools also facilitates policyholders in uploading images and videos directly, enabling an inspection-free claims process. Overall, the integration of AI-based solutions in vehicle insurance provides a faster, accurate, and effective claims experience.

Keywords

Cost Estimation, Deep Learning, Repair vs Replacement, Object Detection, Vehicle Damage Detection, VGG16.

References

  1. R.E. Van Ruitenbeek, and S. Bhulai, “Convolutional Neural Networks for Vehicle Damage Detection,” Machine Learning with Applications, vol. 9, pp. 1-12, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. Van-Dung Hoanga et al., “Powering AI-driven Car Damage Identification based on VeHIDE Dataset,” Journal of Information and Telecommunication, vol. 9, no. 1, pp. 24-43, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  3. Reshma Totare et al., “Car Damage Detection and Price Prediction Using Deep Learning,” International Research Journal of Engineering and Technology (IRJET), vol. 10, no. 1, pp. 458-461, 2022.
    [    Google Scholar] [Publisher Link]
  4. Najmeddine Dhieb et al., “A Very Deep Transfer Learning Model for Vehicle Damage Detection and Localization,” 2019 31st International Conference on Microelectronics (ICM), Cairo, Egypt, pp. 158-161, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Jihad Qaddour, and Syeda Ayesha Siddiqa, “Automatic Damaged Vehicle Estimator using Enhanced Deep Learning Algorithm,” Intelligent Systems with Applications,” Intelligent Systems with Applications, vol. 18, pp. 1-14, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. Daniel Widjojo, Endang Setyati, and Yosi Kristian, “Integrated Deep Learning System for Car Damage Detection and Classification Using Deep Transfer Learning,” 2022 IEEE 8th Information Technology International Seminar (ITIS), Surabaya, Indonesia, pp. 21-26, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  7. Nisaja Fernando et al., “Automated Vehicle Insurance Claims Processing using Computer Vision, Natural Language Processing,” 2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka, pp. 124-129, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  8. Dimitrios Mallios et al., “Vehicle Damage Severity Estimation for Insurance Operations Using In-The-Wild Mobile Images,” IEEE Access, vol. 11, pp. 78644-78655, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. G.S. Mandara, and Prashant Ankalkoti, “Car Damage Assessment for Insurance Companies,” International Journal of Advanced Research in Science, Communication and Technology, vol. 2, no. 6, pp. 431-436, 2022.
    [CrossRef] [Publisher Link]
  10. Mahboub Parhizkar, and Majid Amirfakhrian, “Recognizing the Damaged Surface Parts of Cars in the Real Scene Using a Deep Learning Framework,” Mathematical Problems in Engineering, vol. 2022, pp. 1-7, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  11. Phyu Mar Kyu, and Kuntpong Woraratpanya, “Car Damage Detection and Classification,” Proceedings of the 11th International Conference on Advances in Information Technology, Bangkok Thailand, pp. 1-6, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Kalpesh Patil et al., “Deep Learning Based Car Damage Classification,” 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico, pp. 50-54, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. Ahmed Elbhrawy, Mohamed Belal, and Mohamed Sameh Mostafa Hassanein, “CES: Cost Estimation System for Enhancing the Processing of Car Insurance Claims,” Journal of Computing and Communication, vol. 3, no. 1, pp. 55-69, 2024.
    [    Google Scholar]
  14. Fazli Imaam et al., “Moderate Automobile Accident Claim Process Automation Using Machine Learning,” 2021 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp. 1-6, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  15. Donggeun Lee, Juyeob Lee, and Eunil Park, “Automated Vehicle Damage Classification using the Three-quarter View Car Damage Dataset and Deep Learning Approaches,” Heliyon, vo. 10, no. 14, pp. 1-14, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  16. Namam A. Mohammed, Moayad Y. Potrus, and Abbas M. Ali, “Deep Learning Based Car Damage Classification and Cost Estimation,” Zanco Journal of Pure and Applied Sciences, vol. 35, no. 1, pp. 1-9, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  17. A. Jaya Sree et al., “Vehicle Insurance Damage Detection,” International Journal for Research in Applied Science and Engineering Technology, vol. 11, no. 3, pp. 1985-1988, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  18. Shubham Bhoite et al., “Vehicle Damage Analysis,” International Journal for Research in Applied Science and Engineering Technology, vol. 12, no. 4, pp. 2197-2201, 2024.
    [    CrossRef] [Publisher Link]
  19. M. Sneha et al., “Vehicle Damage Level Estimator for Claiming Insurance Using AI,” International Journal of Novel Research and Development, vol. 9, no. 4, pp. f523-f528, 2024.
    [Publisher Link]
  20. Narra Suma, and N.J Savitha, “Deep Learning-based Damage Detection and Repair Cost Estimation for Automobiles,” International Journal of Innovative Science and Research Technology, vol. 8, no. 6, pp. 717-726, 2023.
    [    CrossRef] [Publisher Link]
  21. Sarang Kadakia et al., “Dent Detection and Price Prediction for Cars: A Comprehensive Analysis,” International Journal of Creative Research Thoughts, vol. 11, no. 10, pp. f312-f319, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  22. Atharva Kasar et al., “Car Damage Detection Using Computer Vision,” International Journal of Emerging Technologies and Innovative Research, vol. 10, no. 11, pp. b768-b771, 2023.
    [Publisher Link]
  23. Vaibhav Agarwal et al., “Damage Assessment of a vehicle and Insurance Reclaim,” International Journal of Creative Research Thoughts, vol. 10, no. 4, pp. e197-e201, 2022.
    [    Google Scholar] [Publisher Link]
  24. Abhishek Khinde et al., “Car Damage Detection Using CNN Algorithm,” International Journal of Creative Research Thoughts, vol. 11, no. 11, pp. e440-e446, 2023.
    [    Publisher Link]