Authors :
Narra Suma; Savitha N. J.
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/y7ak6742
DOI :
https://doi.org/10.5281/zenodo.8081874
Abstract :
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.
Keywords :
MobileNetV2, Deep learning, Convolutional Neural Networks, VGG-16.
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.
Keywords :
MobileNetV2, Deep learning, Convolutional Neural Networks, VGG-16.
