Year: 2025
Type: article
Abstract: Vehicle damage detection is an essential task in automotive assessment, insurance claim processing, and fleet management. Traditional methods involve manual inspection, which is time-consuming and prone to errors. This paper presents an automated damage detection approach utilizing YOLOv8 (You Only Look Once version 8), a state-of-the-art deep learning model for object detection. Our methodology involves training the model on a dataset comprising images of vehicles with and without damage, using supervised learning techniques. The model achieves high detection accuracy and efficiency, making it suitable for real-world applications. This study compares YOLOv8 with previous versions and alternative models to highlight improvements in speed and precision. The findings suggest that this approach can significantly enhance vehicle assessment processes, reducing human effort and improving consistency in damage evaluation.
Authors: Vanathi. B., Akshaya. R., Alfina. P., Gayathri. V., Lekhasri. S.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4408846406