Abstract
In this paper, we worked on the problem of vehicle damage classification/detection which can be used by insurance companies to automate the process of vehicle insurance claims in a quick fashion. The recent advances in computer vision largely due to the adoption of fast, scalable and end-to-end trainable convolutional neural networks make it technically feasible to recognize vehicle damages using deep convolutional networks. We manually collected and annotated images from various online sources containing different types of vehicle damages. Due to the relatively small size of our dataset, we used models pre-trained on a large and diverse dataset to avoid overfitting and learn more general features. Using CNN models pre-trained on ImageNet dataset and using several other techniques to improve the performance of the system, we were able to achieve top accuracy of 96.39%, significantly better than the current results in this work. Furthermore, to detect the region of damage, we used state-of-the-art YOLO object detector and achieving a maximum map score of 77.78% on the held-out test set, demonstrating that the model was able to successfully recognize different vehicle damages. In addition to this, we also propose a pipeline for a more robust identification of the damage in vehicles by combining the tasks of classification and detection. Overall, these results pave the way for further research in this problem domain, and we believe that collection of a more diverse dataset would be sufficient to implement an automated vehicle damage identification system in the near future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Uijlings JRR, van de Sande KEA, Gevers T, Smeulders AWM (2013) Selective search for object recognition. Int J Comput Vision 104:154–171
Girshick R (2015) Fast R-CNN. In: The IEEE international conference on computer vision (ICCV)
Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137–1149
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Redmon J, Farhadi A (2018) Yolov3: an incremental improvement
Jayawardena S (2013) Image based automatic vehicle damage detection. Ph.D. thesis, College of Engineering and Computer Science (CECS)
Gontscharov S, Baumgartel H, Kneifel A, Krieger K-L (2014) Algorithm development for minor damage identification in vehicle bodies using adaptive sensor data processing. Procedia Technol 15:586–594. 2014. 2nd international conference on system-integrated intelligence: challenges for product and production engineering
Multi-camera vision system inspects cars for dents caused by hail
Cha Y-J, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civ Infrastruct Eng 32(5):361–378
Cha Y-J, Chen J, Büyüköztürk O (2017) Output-only computer vision based damage detection using phase-based optical ow and unscented kalman filters. Eng Struct 132:300–313
Mohan A, Poobal S (2018) Crack detection using image processing: a critical review and analysis. Alex Eng J 57(2):787–798
Patil K, Kulkarni M, Sriraman A, Karande S (2017) Deep learning based car damage classification. In: 2017 16th IEEE international conference on machine learning and applications (ICMLA), pp 50–54
Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems, vol 27. Curran Associates, Inc., Red Hook, pp 3320–3328
Oquab M, Bottou L, Laptev I, Sivic J (2014) Learning and transferring mid-level image representations using convolutional neural networks. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., Red Hook, pp 1097–1105
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324
Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Jin J, Dundar A, Culurciello E (2014) Flattened convolutional neural networks for feedforward acceleration. arXiv:1412.5474
Hubara I, Courbariaux M, Soudry D, El-Yaniv R, Bengio Y (2018) Quantized neural networks: training neural networks with low precision weights and activations. J Mach Learn Res 18(187):1–30
Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) XNOR-Net: ImageNet classification using binary convolutional neural networks. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision ECCV 2016. Springer, Zurich, pp 525–542
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861
Sifre L (2014) Rigid-motion scattering for image classification. Ph.D. thesis
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Smith LN (2017) Cyclical learning rates for training neural networks. In: 2017 IEEE winter conference on applications of computer vision (WACV), pp 464–472
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision ECCV 2014. Springer, Zurich, pp 818–833
Fastai deep learning course lesson 1. https://course.fast.ai/videos/?lesson=1
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dwivedi, M. et al. (2021). Deep Learning-Based Car Damage Classification and Detection. In: Chiplunkar, N.N., Fukao, T. (eds) Advances in Artificial Intelligence and Data Engineering. AIDE 2019. Advances in Intelligent Systems and Computing, vol 1133. Springer, Singapore. https://doi.org/10.1007/978-981-15-3514-7_18
Download citation
DOI: https://doi.org/10.1007/978-981-15-3514-7_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3513-0
Online ISBN: 978-981-15-3514-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)