Car damage detection is an important field of research due to its potential application in accident prevention, insurance claims, and law enforcement. In this paper, we propose a deep learning approach for car damage detection using image analysis. We use a pre-trained convolutional neural network (CNN) model to extract features from the input images and then train a support vector machine (SVM) classifier on the extracted features. The proposed approach is evaluated on a publicly available dataset of car damage images and achieves a high accuracy of 94% in car damage detection. Our results demonstrate the effectiveness of deep learning in car damage detection and have potential for real- world applications

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Car damage classification CNN transfer learning convolutional auto-encoders