Deep Learning Based Car Damage Classification and Cost Estimation

Namam A. Mohammed; Moayad Y. Potrus; Abbas M. Ali

doi:10.21271/ZJPAS.35.1.1

Authors

Namam A. Mohammed Department of software and informatics Engineering, College of Engineering Salahaddin University-Erbil, Kurdistan Region, Iraq
Moayad Y. Potrus Department of software and informatics Engineering, College of Engineering Salahaddin University-Erbil, Kurdistan Region, Iraq
Abbas M. Ali Department of software and informatics Engineering, College of Engineering Salahaddin University-Erbil, Kurdistan Region, Iraq

DOI:

https://doi.org/10.21271/ZJPAS.35.1.1

Keywords:

Car Damage Detection, Mask RCNN, Deep Learning, Cost of the Damage, Machine Learning

Abstract

Due to the increasing number of people driving cars, the number of insurance claims has also increased. This process involves the manual assessment of the vehicle by an insurance company's service engineer, as well as the physical inspection by a licensed insurance company representative. An end-to-end solution has been proposed that would allow the customer and the insurance company to automate the process of recognizing the damaged area in the vehicles and estimating the cost of the damage. It would allow them to get a better understanding of the condition of the vehicle. For this purpose, A deep learning, Mask Region-based Convolutional Neural Network (Mask RCNN) model was utilized in this work to classify vehicle damages costs. Two Mask RCNN models were utilized, the first one was used to detect the sides of the vehicle, which will affect damage cost estimation and the second was used to find the area of the damage. The Experimental work shows that the proposed model gives reasonable results to estimate the cost of the damage. We achieve an accuracy of 98.5% with the combination of the two Mask RCNN models. And showed that Mask RCNN has a promising result to detect the area of the damage in the car.

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