YOLOv8-Powered Real-Time Car Damage Detection

The increased interest in automated and precise vehicle damage detection tasks has influenced the advances of deep learning and computer vision technologies. This research presents a novel method for automatic vehicle damage assessment based on YOLOv8, one of the latest and powerfull detection models. The model's aim is to assist users in inspecting vehicles in cases where different category of damage such as broken lights, door and others are present. It is done by accurately identifying and classifying the damages without depending on manual effort which is produce issues like inaccurate and time consuming. For eight image damage categories extensive dataset has been prepared, that was used for modeling. To achieve good-quality models, the Roboflow tool was employed for the purpose of picture resizing and augmentation, which assures high-quality training and therefore enhances models. As the main tool for damage detection, YOLOv8 has also been trained over 5000 epochs to detect more complex images. The cumulated metrics include mean Average Precision, precision, and recall. From the data collected, it was established that the YOLOv8 model has been efficient in detecting damages in vehicles with enhanced ability than there was in previous versions of the model. This automated solution is particularly promising for use in the insurance and automobile damage assessment industries as it makes damage assessments more accurate and faster. There are future applications for this model and also made suggestions including increasing the dataset to account for more damage types and using techniques like transfer learning to increase the accuracy of the system.