Deep Learning-based Damage Detection and Repair Cost Estimation for Automobiles

In the automotive sector, estimating the cost
to repair damaged vehicles is a critical duty. In this
project, we present a technique for estimating
maintenance costs that makes use of the cutting-edge
deep learning architecture MobileNetV2. With the use of
a sizable dataset of photos of damaged vehicles and
related repair costs, we fine-tune the pre-trained
MobileNetV2 model. For insurance companies, repair
facilities, and automobile manufacturers, the suggested
method can be used as a cost-effective and practical
option to determine the expense of repairing damaged
vehicles.