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首页> 外文会议>2017 National Information Technology Conference >Image processing based severity and cost prediction of damages in the vehicle body: A computational intelligence approach
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Image processing based severity and cost prediction of damages in the vehicle body: A computational intelligence approach

机译：基于图像处理的车身损坏严重性和成本预测：一种计算智能方法

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摘要

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：车辆损坏检测是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在驾驶员和保险公司发生事故时识别车辆的损坏，并由保险公司根据损坏和车辆租赁公司来检测和确定适当的评估，以将车辆的损坏分配给驾驶员。有罪的客户。该系统的核心技术是对象识别。然而，对象识别和分类是困扰研究范围的问题，这种性质的项目的可靠性在于特征选择和提取机制。本文提出了一种使用2D图像的车身损伤严重程度和成本预测的新方法。因此，一旦车身损坏，驾驶员不必等到保险公司进行评估之后，他本人就可以很简单地知道恢复损坏的费用。上载图像后，系统将处理图像并识别凹痕。接下来，考虑到车辆的特征（如品牌，型号和制造年份），将其分类为相关的严重性等级。然后，将根据损坏图像生成的严重性与成本规则进行映射，该成本规则是根据车辆的属性（例如品牌，型号和制造年份）构建的。最后，用户会收到损坏严重程度等级以及可以从中恢复损坏的平均成本的通知。

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