Publication Date: 2023/06/26
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.
DOI: https://doi.org/10.5281/zenodo.8081874
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23JUN027.pdf
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