• Atharva Kasar
• Apurva Kshirsagar
• Geeta Hade
• Nishant Khandhar
• Priti Warungse

This research paper presents a comprehensive framework for car damage detection using deep learning techniques. The proposed system aims to address the critical need for Precise, effective, and automated techniques for evaluating vehicle damage, with potential applications in insurance claims processing, vehicle maintenance, and accident analysis. The project leverages a state-of the-art convolutional neural network (CNN) the architectural design, particularly in the case of ResNet for its outstanding feature extraction prowess and classification performance. The deep learning model is trained on a carefully curated dataset of vehicle images, annotated with labels indicating the presence and severity of damage. The project undergoes rigorous testing and validation to assess its accuracy, precision, recall, and F1-score. User feedback and user experience evaluations are considered for continuous improvement. The resulting system demonstrates the potential to significantly streamline car damage assessment processes, reduce human error, and expedite insurance claim settlements. The project undergoes rigorous testing and validation to assess its metrics encompassing accuracy, precision, recall, and F1-score.

Computer Vision, Image Processing, Machine Learning (ML), Deep Learning (DP), Convolutional Neural Network (CNN), Feature Extraction, Object Detection, Region Of Interest (ROI), Anomaly Detection), Segmentation, Paging, Classification, Accuracy, Precision, Recall, F1-Score, Training and Testing Dataset, ROI Localization, Real-Time Detection, Transfer Learning, Data Augmentation

"Car Damage Detection Using Computer Vision", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.b768-b771, November-2023, Available :http://www.jetir.org/papers/JETIR2311193.pdf

"Car Damage Detection Using Computer Vision", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppb768-b771, November-2023, Available at : http://www.jetir.org/papers/JETIR2311193.pdf