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Auto Assure : Deep Learning-Based Automated Car Insurance Inspector: Damage Location Detection and Severity Assessment

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dc.contributor.author De Silva, Thiran
dc.date.accessioned 2025-06-16T11:17:46Z
dc.date.available 2025-06-16T11:17:46Z
dc.date.issued 2024
dc.identifier.citation De Silva, Thiran (2024) Auto Assure : Deep Learning-Based Automated Car Insurance Inspector: Damage Location Detection and Severity Assessment. BSc. Dissertation, Informatics Institute of Technology en_US
dc.identifier.issn 20200829
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/2603
dc.description.abstract In the contemporary landscape of car insurance assessment, technological integration has emerged as a pivotal force. This abstract presents a pioneering application aimed at optimizing the assessment process for both car owners and insurance entities. By allowing seamless upload of damaged vehicle images, the system conducts detailed analysis, extracting vital information such as vehicle identification numbers, damage severity, and overall condition. Utilizing cutting-edge computer vision algorithms, notably the Inception v3 model, the system ensures precise and reliable analysis, markedly enhancing assessment efficiency and accuracy. Moreover, a real-time feedback mechanism enables continuous model refinement through reinforcement learning techniques. This innovative paradigm not only enriches user experience but also guarantees the system's adaptability and enduring efficacy in catering to evolving demands of car owners and insurance stakeholders en_US
dc.language.iso en en_US
dc.subject Reinforcement Learning en_US
dc.subject Inception v3 en_US
dc.title Auto Assure : Deep Learning-Based Automated Car Insurance Inspector: Damage Location Detection and Severity Assessment en_US
dc.type Thesis en_US


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