An Intelligent System to Assess the Exterior Vehicular Damage based on DCNN

K. Meenakshi, S. Sivasubramanian
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Abstract

In today’s world, with all the technological advancements that we have made, the process of car damage detection is a very lengthy process involving a lot manual work. It requires human verification approach which is very slow and leads to an extremely arduous and tardy process, which leads to human error creeping into the results, thereby increasing the hardships of the common man. Proliferation of Indian automobile industry is directly proportional to the car incidents which is also directly proportional to more insurance claims. Insurance companies need to cover many simultaneous claims and solve the issue related to claims leakage. We explore different DNN based techniques for the purpose of the vehicle damage detection which will completely eliminate the large amount of paper work and man power for physical damage estimation and shift this entire process to an efficient AI based solution which can provide a rapid claim process in a shorter time span. The proposed model provides high accuracy confidence scores for the detected damages which are classified on the basis of the 21 vehicle damage classes that we have defined so that there can be an extensive segregation of damages incurred by the vehicle.
基于DCNN的车辆外部损伤智能评估系统
在当今世界,随着我们所取得的所有技术进步,汽车损伤检测的过程是一个非常漫长的过程,涉及大量的人工工作。它需要非常缓慢的人工核查方法,并导致一个极其艰巨和缓慢的过程,这导致人为错误潜入结果,从而增加了普通人的苦难。印度汽车工业的扩散与汽车事故成正比,这也与更多的保险索赔成正比。保险公司需要同时承保许多索赔,并解决与索赔泄漏相关的问题。我们探索了不同的基于深度神经网络的车辆损伤检测技术,这将完全消除物理损伤估计的大量纸张工作和人力,并将整个过程转移到一个高效的基于人工智能的解决方案,该解决方案可以在更短的时间内提供快速的索赔过程。所提出的模型为检测到的损害提供了高精度的置信度分数,这些损害是根据我们定义的21个车辆损害类别进行分类的,因此可以对车辆造成的损害进行广泛的隔离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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