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Image processing based severity and cost prediction of damages in the vehicle body: A computational intelligence approach

机译：基于图像处理车身损坏的严重程度和成本预测：计算智能方法

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Vehicle damage detection is one of the important prime activities in the insurance and vehicle rental industries. These kinds of systems are widely used to identify the damage of a vehicle once an accident happens by the driver and also by the insurance company to detect and determine a suitable appraisal as per damage and vehicle rental companies to assign the damage of a vehicle to a guilty customer. The core technique of this system is object recognition. However, object recognition and classification being perplexing research ranges, the reliability of a project of this nature lies in the feature selection and extraction mechanisms. This paper presents a novel approach of vehicle body damage severity and cost prediction with using 2D images. Thus once vehicle body damages, the driver does not have to wait until the insurance company calculates the appraisal, he himself can get a brief idea as to how much will it cost to recover the damage. Once an image is uploaded, the system processes the image and identifies the dent. Next, it is classified into the relevant severity class also considering the features of the vehicle like the make, model and the year of manufacture. Afterward, the severity generated as per damage image is mapped with the cost rules, which are constructed based on the properties of the vehicle such as the make, model and the year of manufacture. Finally, the user gets notified with a damage severity class and an average cost from which the damage can be recovered.
机译：车辆损坏的检测是在保险和汽车租赁行业的重要主要活动之一。这些类型的系统被广泛用于识别车辆的损害一旦发生事故由驾驶员也由保险公司来检测并确定合适的评价按伤害和车辆租赁公司指定车辆的损坏有罪的客户。该系统的核心技术是对象识别。然而，目标识别和分类存在错综复杂的研究范围内，这种性质就在于特征选择和提取机制的一个项目的可靠性。本文呈现车体损伤的严重程度和成本预测的使用2D图像的新方法。因此，一旦车身损坏，驾驶员不必等到保险公司计算的评价，他自己也获得了短暂的主意，因为这将花费多少恢复的损害。一旦图像被上传时，系统处理该图像，并且识别凹痕。其次，它分为相关程度类也考虑像品牌，型号和制造年份车辆的功能。然后，按照损害图像生成的严重程度被映射与成本规则，这是基于所述车辆的性能，如制造商，型号和制造年份构成。最后，用户获取与一个损伤严重性等级和从该损伤可以被回收的平均费用通知。

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