Enhancing insurance assessments Deep learning for vehicle damage intensity prediction from unconstrained mobile imagery
Dr. T. K. P. Rajagopal, D. Arun, V.R .Lakshman, B. Manikandan, M. Mohamed Zaid
Dr. T. K. P. Rajagopal Department of Computer Science and Engineering Hindusthan College of Engineering and Technology
E-mail: rajagopal.cse@hicet.ac.in
D. Arun Department of Computer Science and Engineering Hindusthan College of Engineering and Technology
E-mail: 20104076@hicet.ac.in
V.R .Lakshman Department of Computer Science and Engineering Hindusthan College of Engineering and Technology
E-mail: 201040111@hicet.ac.in
B. Manikandan Department of Computer Science and Engineering Hindusthan College of Engineering and Technology
E-mail: 201040117@hicet.ac.in
M. Mohamed Zaid Department of Computer Science and Engineering Hindusthan College of Engineering and Technology
E-mail: 201040119@hicet.ac.in
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Abstract - In the realm of insurance assessments, accurately determining vehicle damage intensity is pivotal for fair and efficient claim processing. Traditional methods often struggle with the variability inherent in unconstrained mobile imagery, leading to subjective evaluations and prolonged processing times. Leveraging deep learning techniques, this study proposes a novel framework aimed at enhancing insurance assessments by predicting vehicle damage intensity directly from unconstrained mobile imagery. Our approach integrates convolutional neural networks (CNNs) to extract intricate features and learn complex patterns from diverse image data. By harnessing the power of deep learning, we strive to provide insurers with a robust tool capable of swiftly and accurately assessing vehicle damage severity, thereby streamlining claim procedures and improving customer satisfaction.
Key Words: Deep learning, convolutional neural networks, Streamlit, Insurance
