Preventing Car Damage using CNN and Computer Vision
Avinash Sharma1, Aaditi Verma2, Dhananjay Gupta3

1Dr.Avinash Sharma*, Professor, Maharishi Markandeshwar Engineering College, Mullana, Ambala (Haryana) Constituent Institution of Maharishi Markandeshwar University, Mullana is NAAC accredited ‘A’ Grade Deemed University.
2Aaditi Verma*, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR, India.
3Dhananjay Gupta, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR, India. 

Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 2751-2755 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5020119119/2019©BEIESP | DOI: 10.35940/ijitee.A5020.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This research contain convolutional neural network are used to recognize whether a car on a given image is damage or not, from where it is damage and severity of the damage. Using transfer learning to take advantage of available models that are trained on a more general object recognition task, very satisfactory performance has been achieved, which indicate great opportunities of this approach. Car accidents are stressful and the auto claims process is ripe for disruption. Using computer vision to accurately classify vehicle damage and facilitate claims triage.
Keywords: Car Damage, Convolutional Neural Network, Neural Network
Scope of the Article: Neural Information Processing