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

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.