Abstract: Vehicle insurance processing using images is a critical sector with a lot of room for automation. In this study, we look at the topic of car damage detection. Vehicle damage detection. Using images taken at the site of an accident can save time and money when filing insurance claims, as well as provide more convenience for drivers. Artificial Intelligence (AI) in the sense of machine learning and deep learning algorithms can assist in solving problems. A vehicle-damage-detection technique based on transfer learning and a mask regional convolutional neural network (Mask RCNN) are utilized to quickly handle accident compensation problems. The algorithms identify the damaged section of a car, determine its position, and then estimate the severity of the damage. Very satisfactory results have been produced using transfer learning to take advantage of available models that have been trained on a more generic object identification challenge.

Keywords: Car Damage Detection, Machine learning, Prediction, Mask RCNN, Transfer Learning, Deep Learning

PDF | DOI: 10.17148/IJARCCE.2021.10808

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