Robust Car Damage Identification Through CNN and SVM Techniques

The study project, which centers on the creation and assessment of an advanced model for automobile damage categorization, is summarized in the abstract along with its main conclusions and consequences. To give a full knowledge of the model's efficacy, the study focuses on evaluating its performance across many measures, such as accuracy, precision, recall, F1-score, support, and support percentage. They devote much attention to the discussion of using a dataset of 14,200 images and evaluate the model's accuracy as 93%. 66% overall. The model proposed in this paper demonstrate high mean values of accuracy, recall, and F1-score in predicting many classes of automotive damages: bumper dint, scratch, dint in the door, and smash. According to these parameters, the model has the ability to define various kinds of damages and thus assists the automobile sector to make decisions faster. Continuing in method with the aim of providing more information on the composition and balance of the dataset, there are the support values that give information on how the samples are distributed over different classes. The support percentage statistic, on the contrary, points out how each class is supported in the dataset in relation to the other and points to how good a situation the model is ready to deal with. Finally, the research underlines how using deep learning methods for auto damage classification can provide the necessary tools for effective acceleration of all operations that are related both to insurance claims handling and cars maintenance and fleet management. want to help develop stable solutions potentially increasing customer satisfaction and speeding up or improving processes in multiple areas of automotive industries using such methods as state-of-art machine learning algorithms or detailed assessment.