• Jeevan N V
• K R Sumana

The automotive industry faces numerous challenges, one of which is the efficient assessment of vehicle damage. Traditional methods rely heavily on manual inspection, which can be subjective and prone to human error. With the advent of artificial intelligence and machine learning, there is a significant opportunity to revolutionize this process through automated systems. This project, titled "Car Damage Detection using Yolov8," leverages the power of deep learning to develop a robust and accurate system for detecting and categorizing car damage from images. Utilizing a comprehensive dataset from Roboflow, the system is trained to identify various types of car damage. This project not only aims to streamline the damage assessment process but also to provide a reliable tool for insurance companies, repair shops, and car owners.

Deep Learning, Vehicle Damage Detection, Vehicle Images, YOLOv8 (You Only Live Once), Condition Monitoring

"Enhancing Robustness And Generalization In Vehicle Damage Detection Through Deep Neural Networks ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.i132-i136, July-2024, Available :http://www.jetir.org/papers/JETIR2407816.pdf

"Enhancing Robustness And Generalization In Vehicle Damage Detection Through Deep Neural Networks ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppi132-i136, July-2024, Available at : http://www.jetir.org/papers/JETIR2407816.pdf