Enhanced Car Damage Classification: A Fusion of Deep Learning and Random Forest Methods

The abstract presents several performance metrics, such as precision, recall, F1-score, support proportion, as well as accuracy, and summarizes the main conclusions of a study on the categorization of vehicle damage. The goal of the study was to categorize auto damage into five different groups: door dent, smash, glass shatter, scratch, and bumper dent. The precision values of the model, which vary from 94.34% to 95.59%, show how accurate it is at properly identifying instances in each class. Recall rates higher than 94% also demonstrate how well the model finds relevant occurrences in all categories. 12100 photos made up the dataset that was used for training and assessment. The model has an overall accuracy of 95.04%. F1 scores, which routinely exceed 95%, demonstrate the harmony between recall and precision, which is necessary for accurate classification. Support values provide information about how instances are distributed throughout the dataset and show that each class is well-represented. When taking into account the various support proportions of the various car damage categories, the model's consistent & balanced performance is highlighted by its weighted average precision, recall, or F1-score, all of which stand at 95.04%. The 98% accuracy rate highlights the model's general ability to classify auto damage properly. These results point to prospective uses in fields where accurate and efficient car damage assessment is essential, like processing insurance claims and auto repairs. By demonstrating the model's generalizability & applicability for a variety of real-world circumstances, the study advances automation or optimization within the automobile industry. Overall, the research findings are succinctly summarized in the abstract, which also highlights the model's strong performance and possible uses in relevant industries.