CAR Damage Price Predictor

Abstract

The automotive repair industry is evolving, and with that comes increasing demand for damage assessment's accuracy and efficiency. In this project, we propose a web platform for predicting car damage severity and repair costs using state-of-the-art machine learning and deep learning techniques. The platform uses Mobile Net-a light-weight convolutional neural network-for efficient and accurate image classification. The website allows users to upload uploaded images of damaged cars to view fast evaluation on damages classified into either high, medium, or low, along with detailed estimates of repair costs. The system allows a smooth upload with SQLite for safe data management while providing better prediction using transfer learning and pre-trained models. Faster R-CNN and Mask R-CNN are also applied for precise localization and instance segmentation. This novel method is envisioned as a technology that will transform car repair by providing a credible, effective, and accessible tool for automated damage assessment that lets vehicle owners decide with time and resource savings. The platform achieved remarkable diagnostic accuracy at up to 95%, thus significantly reducing false positives and negatives while offering advice to the car owner and car repair professionals.