Abstract
In the world of vehicle insurance and rental industries, detecting damages on vehicles is one of the most important activities. These damages are identified and inspected by the drivers and insurance companies to determine the suitable monetary compensation, and by vehicle rental companies to assign the responsibility to guilty customers. Since the current system is time consuming, wherein the inspectors have to manually inspect the damages before appraising, this identification can be performed by object recognition systems. The intricacy of these systems rests in the image feature determination and extraction techniques. A more novel approach to detecting the severity of damage and predicting the repair costs is using 2D image recognition. This way, the driver would not have to wait for the appraisal of insurance companies to determine the rough estimate of cost of repairs. After a picture is uploaded to the framework, the picture is then processed and the vehicular damage is identified. The image is then classified into relevant damage severity classes. Afterward, the damage severity that is detected in the image, is then mapped to the approximate cost values. Then finally, the user is presented with a report of the vehicular damage severity classification as well as an average expense cost report from which the vehicle can then be recuperated from the damages.
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Kalshetty, J.N., Hrithik Devaiah, B.A., Rakshith, K., Koshy, K., Advait, N. (2022). Analysis of Car Damage for Personal Auto Claim Using CNN. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_25
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DOI: https://doi.org/10.1007/978-981-16-5157-1_25
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