Abstract: In the transportation industry, a significant challenge pertains to the assessment of vehicle damage. The conventional manual inspection process is time-consuming and inefficient. This creates an opportunity for automation in the vehicle insurance sector, particularly in the utilization of image-based methods for expediting claims processing. Leveraging photographs taken at the scene of accidents can streamline the entire process, resulting in cost savings, enhanced driver convenience, and improved overall efficiency.

In the realm of the automobile insurance industry, a substantial financial resource is currently allocated to address claims leakage, which is the disparity between the most favourable and the real settlement of insurance claims. Traditional practices predominantly rely on visual examination and validation methods to mitigate claims leakage. However, these inspection processes often prove to be time-consuming and contribute to the delay in claims processing. The implementation of an automated system for inspection and validation presents a valuable opportunity to expedite this crucial process, thereby enhancing overall operational efficiency and ensuring a more streamlined claims settlement procedure.

Keywords: Vehicle damage assessment, Claims processing, Transfer learning, Pre-trained VGG, Deep Learning.

Prof. Ravindra Mule, Sushain Gupta, Abhishek Thakare, Sahil Savardekar, Jigar Sable, "Advancements in Computer Vision for Car Damage Detection and Assessment: A Comprehensive Study", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13108.

PDF | DOI: 10.17148/IJARCCE.2024.13108

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