International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Damage Detection of An Automobile

Author : R Melvin Raj ¹ R Jyosthna ² N Ramya Madhuri ³ R Akshay Sunny ⁴ A Pratima ⁵

Date of Publication :20th October 2022

Abstract: As a result of the proliferation of automobile in- dustries today. There have been an increasing number of car accidents, not all of which are serious, but the automobile is damaged. Detecting automobile damage at the site of an accident using images is exceptionally beneficial as it may significantly lower the cost of processing the insurance reimbursement process while also providing more convenience to automobile users. In most cases, this damage is detected and assessed manually from the carâ€™s images during the car evaluation process. In this paper, we worked on the problem of automation of vehicle damage detection which can be used by insurance companies to automate the process of vehicle insurance claims in a rapid 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 semantic segmentation. We manually collected and annotated images from various online sources containing different types of vehicle damages and we used U-NET architec- ture to detect the damage of an automobile.

Reference :

1. Phyu Mar Kyu and Kuntpong Woraratpanya. 2020. Car Dam- age Detection and Classification. In Proceedings of International Conference on Advances in Information Technology (IAIT2020), July 1-3, 2020, Bangkok, Thailand. ACM, New York, NY, USA,.https://doi.org/10.1145/34066 01.3406651.
2. Ying & Dorai, Chitra. (2007). Applying Image Analysis to Auto Insurance Triage: A Novel Application. 280 - 283. 10.1109/MMSP.2007.4412872.
3. U. Waqas, N. Akram, S. Kim, D. Lee and J. Jeon, ”Vehicle Dam- age Classification and Fraudulent Image Detection Including Moire´ Effect Using Deep Learning,” 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2020, pp. 1-5, doi: 10.1109/CCECE4 7787.2020.9255806.
4. Aggregated Residual Transformations for Deep Neural Networks, Sain- ing Xie1 Ross Girshick2 Piotr Dollar 2 Zhuowen Tu1 Kaiming He2 1UC San Diego 2Facebook AI Research.
5. Deep Residual Learning for Image Recognition. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
6. U-Net: Convolutional Networks for Biomedical Image Segmentation. Olaf Ronneberger, Philipp Fischer & Thomas Brox.