Abstract:

Vehicles have an impact on people’s daily safety, and because there are so many different types and sizes of materials, it can be challenging to distinguish and detect the conditions around the vehicle. In this project, we looked into the matter of car damage classification and detection, which insurance providers can utilize to quickly, automates the handling of vehicle insurance disputes.

Deep convolutional networks can be used to detect car damage and with recent developments in computer vision, which are largely attributable to the implementation of quick, scalable, and entire trainable CNN. There is a huge number of accidents prevailing in all urban and rural areas. Patterns involved with different circumstances can be detected by developing an accurate prediction models which will be capable of automatic separation of various accidental scenarios. Hence we are proposing a method that which can predict either the car is undergone with the accident or not. This process is performed using the CNN based transfer learning algorithm (MobileNet) of deep learning. In this study, we will look at the topic of car damage detection. Vehicle damage detection and cost estimation of vehicle. Using pictures taken at the scene of an accident can save time and money when filing insurance claims, as well as provide more convenience to drivers.
Artificial intelligence (AI) in the sense of machine learning and deep learning algorithms can help solve problems.

Keywords:
Convolutional Neural Network,
Artificial Intelligence,
Deep Learning

Objective:

Predict either the car is undergone with the accident or not.and this process is performed using the CNN based transfer learning algorithm of deep learning.and cost estimation for claiming Insurance. If the given input is fifteen years old car then an alert SMS will be sent to the user.
purposes is to streamline the process of evaluating vehicle damage and estimating repair costs. By leveraging advanced technologies such as computer vision, machine learning, and data analytics, the system aims to:

Automate Assessment : Eliminate or reduce the need for manual inspection by insurance adjusters or vehicle repair specialists. This can speed up the claims process and improve efficiency.
Accuracy:Provide accurate assessments of vehicle damage to ensure fair and precise insurance payouts. By leveraging AI algorithms, the system can analyze images or videos of the damaged vehicle to identify and quantify the extent of damage.