Car Damage Detection using CNN

Car Damage Detection using CNN

Mr. Rahul Dhokane1, Vidya Mhaske2, Gayatri Pekhale3, Shraddha Tajane4, Akshata Shinde5

1Assistant Professor, Department of Information Technology, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India

2,3,4,5Department of Information Technology, Sir Visvesvaraya Institute of Technology, Nashik,

Maharashtra, India

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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 of damage to a car, enabling insurance companies to expedite claims processing without the need for time-consuming and resource-intensive manual verification.

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, a pre-trained framework, was employed in the development of the object detection model, emphasizing efficiency and reliability in the insurance claims process.

Key Words: E-commerce, Car Damage, Detection, Classification, VGG, Mask RCNN, Severity, Location, Masking