Abstract
Today, the high competition among domestic automobile manufacturers is intense situation than previous years. This result gives advantages in a good variety of brands, models, engine sizes and appearances. This can cause some critical issues in recognizing and recalling a car by manufacturer. In addition, an owner may modify some parts of original vehicle such as the head bumper, the rear bumper, and the head light. This modification also affects the people who are looking for pre-owned cars. Despite the fact, the details are mismatch with the vehicle registration book that issued by the Department of Land Transport. From this incident, the researchers implemented a convolutional neural network (CNN) in the identification of vehicle characteristics to reduce the ambiguity for each car’s models. The researchers conducted experiments using five algorithms. SVM, ResNet34, ResNet50 and Inception-ResNetV2. The researchers set up a library of two car models, Toyota Hilux and Honda Civic sedan and Civic Hatchback, including models from past ten years ago until the present. The images are of 224 × 224 pixels. The data are categorized into two sets, a training set has 1,449 images which is counted as 80% of total images and a testing set is having 362 images which is about 20% of total. The total images are 1,811 and 26 Classes. Our experiments compared the accuracies of SVM, ResNet34, ResNet50, and Inception-ResNetV2, which came out to be 21.4%, 55.5%, 66.6%, and 92.8% respectively. As a result, Inception-ResNetV2 outperforms among all other methods.
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References
Qassim, H., Verma, A., Feinzimer, D.: Compressed residual-VGG16 CNN model for big data places image recognition. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 169–175 (2018)
Tammina, S.: Transfer learning using VGG-16 with deep convolutional neural network for classifying images. Int. J. Sci. Res. Publ. (IJSRP) 9, 143–150 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (2007)
Venables, W.N., Ripley, B.D.: Modern applied statistics with S-PLUS. Springer, Heidelberg (2013). https://doi.org/10.1007/978-0-387-21706-2
Patil, K., Kulkarni, M., Sriraman, A., Karande, S.: Deep learning based car damage classification. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 50–54 (2017)
Kalshetty, J.N., Hrithik Devaiah, B.A., Rakshith, K., Koshy, K., Advait, N.: Analysis of car damage for personal auto claim using CNN. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, K.-L. (eds.) Sentimental Analysis and Deep Learning. AISC, vol. 1408, pp. 319–329. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-5157-1_25
Dwivedi, M., et al.: Deep learning-based car damage classification and detection. In: Chiplunkar, N.N., Fukao, T. (eds.) Advances in Artificial Intelligence and Data Engineering. AISC, vol. 1133, pp. 207–221. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-3514-7_18
Kyu, P.M., Woraratpanya, K.: Car damage detection and classification. In: Proceedings of the 11th International Conference on Advances in Information Technology, pp. Article 46. Association for Computing Machinery, Bangkok, Thailand (2020)
Dhieb, N., Ghazzai, H., Besbes, H., Massoud, Y.: A very deep transfer learning model for vehicle damage detection and localization. In: 2019 31st International Conference on Microelectronics (ICM), pp. 158–161 (2019)
Pasupa, K., Kittiworapanya, P., Hongngern, N., Woraratpanya, K.: Evaluation of deep learning algorithms for semantic segmentation of car parts. Comp. Intell. Syst. (2021)
Gulli, A., Pal, S.: Deep learning with Keras. Packt Publishing Ltd, Birmingham (2017)
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Chantamit-o-Pas, P., Sangaroon, P., Srisura, J. (2023). Deep Learning Based Automobile Identification Application. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_45
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