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

INTRODUCTION:

Today, one of the first businesses to invest in innovation, cutting-edge technology, and artificial intelligence (AI) is the insurance industry. Car insurance companies spend millions of dollars each year due to evasion of insurance claims in today's society where the number of car accidents is on the rise.. In the insurance industry, Artificial intelligence (AI) based on machine learning and deep learning can assist with challenges including data analysis and processing, fraud detection, risk reduction, and claim automation. As a result, insurance companies have sought to reduce the time it takes to analyze damage and settle claims. However, developing current applications to solve these problems remains difficult, especially when using deep learning to assess car damage. Deep learning is an effective method for tackling complicated problems, but it necessitates more resources for model building, i.e., deep learning demands a large dataset and takes longer to compute.

A traffic collision, also called a motor vehicle collision, car accident or car crash, occurs when a vehicle collides with another vehicle, pedestrian, animal, road debris, or other stationary obstruction, such as a tree, pole or building. Traffic collisions often result in injury, disability, death, and property damage as well as financial costs to both society and the individuals involved. Road transport is the
most dangerous situation people deal with on a daily basis, but casualty figures from such incidents attract less media attention than other, less frequent types of tragedy.
A number of factors contribute to the risk of collisions, including vehicle design, speed of operation, road design, weather, road environment, driving skills, impairment due to alcohol or drugs, and behavior, notably aggressive driving, distracted driving, speeding and street racing.
Human factors in vehicle collisions include anything related to drivers and other road users that may contribute to a collision. Examples include driver behavior, visual and auditory acuity, decision-making ability, and reaction speed. A 1985 report based on British and American crash data found driver error, intoxication, and other human factors contribute wholly or partly to about 93% of crashes.