Abstract
This research develops an automated car damage detection system using computer vision and deep learning algorithms to detect and classify damage on cars. Traditional methods rely on human inspectors, which are time-consuming and costly. The proposed system uses convolutional neural networks and transfer learning on VGG-16 for efficient and accurate detection. The developed pipeline breaks down the process into distinct stages for maximized efficiency and accuracy. This allows for more efficient and informed decision-making by insurance companies, maintenance providers, or individuals. The system provides a more efficient, accurate, and cost-effective way to assess car damages, enhancing road safety with more objective and consistent assessments.
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References
Deng J, Dong W, Socher R, Li L, Li K, Fei L (2009) ImageNet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, pp. 248–255
Dwivedi M, Malik H, Omkar S, Monis E, Khanna B, Samal S, Tiwari A, Rathi A (2021) Deep learning-based car damage classification and detection. In: Advances in artificial intelligence and data engineering. Springer, pp 207–221
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: arXiv preprint, arXiv:1512.03385v1
Huang G, Liu Z, Maaten L, Weinberger KQ (2018) Densely connected convolutional networks. In: arXiv preprint, arXiv:1608.06993
Karen S, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: arXiv preprint, arXiv:1409.1556
Krizhevsky A, Ilya S, Geoffrey H (2017) Imagenet classification with deep convolutional neural networks. In: Communications of the ACM 60, no 6, pp 84–90
Matthew Z, Fergusn R (2014) Visualizing and understanding convolutional networks. In: Computer vision–ECCV 2014: 13th European conference, Zurich, Switzerland, September 6–12, 2014, proceedings, part I 13. Springer International Publishing, pp 818–833
Ministry of Road Transport Highways, Government of India. Accessed 25 March, 2023. https://morth.nic.in/sites/default/files/RA2021Compressed.pd
Moreira JPLM (2017) Identificação de danos em véıculos sinistrados através de imagens. In: U.PORTO. https://repositorio-aberto.up.pt/handle/10216/107814
Putri PK, Putra IKG, Mandenni NIMI (2014) An expert system to detect cardamage by using cart method. J Theor Appl Inf Technol
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going deeper with convolutions. In: arXiv preprint, arXiv:1409.4842
Zhang X, Zhou X, Lin M, Sun J (2017) Going deeper with convolutions. In: arXivpreprint, arXiv:1707.01083v2
Zhang W, Cheng Y, Guo X, Guo Q (2020) Automatic car damage assessment system: reading and understanding videos as professional insurance inspectors. AAAI Conf Artif Intell 34(09):13646–13647. AAAI
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Mistry, V., Jagad, C., Jhunjhunwala, U., Tawde, P. (2024). Intelligent Car Damage Detection System. In: Mehta, G., Wickramasinghe, N., Kakkar, D. (eds) Innovations in VLSI, Signal Processing and Computational Technologies. WREC 2023. Lecture Notes in Electrical Engineering, vol 1095. Springer, Singapore. https://doi.org/10.1007/978-981-99-7077-3_47
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DOI: https://doi.org/10.1007/978-981-99-7077-3_47
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