CAR DAMAGE DETECTION USING CNN

Abstract

In today's modern society, automobiles play a crucial role, and the automatic classification of car damages holds particular significance for the auto insurance industry. Our proposed solution involves the implementation of two Convolutional Neural Network (CNN) models. Specifically, the VGG16 model is employed to identify and assess the location and severity of car damage, while the Mask R-CNN is utilized to accurately mask the damaged regions. Both models collectively provide valuable insights into the extent. The CNN models effectively filter out images without damages, allowing only those with identified damage to be passed on to the object detection model. This strategic approach enhances the overall performance of the model. The core objective of this research project is to achieve maximum accuracy through the utilization of CNN models. TensorFlow, Key Words: E-commerce, Car Damage, Detection, Classification, VGG, Mask RCNN, Severity, Location, Masking