Car Damage Identification Using Deep Learning Techniques
Keywords:
Car Damage Detection, YOLOv8, Deep Learning, Computer Vision, Object Detection, Vehicle Damage Assessment, Image Processing, Insurance Claim Automation, Convolutional Neural Networks (CNN), Mean Average Precision (mAP).Abstract
Car damage assessment plays a vital role in insurance claim processing, vehicle maintenance, and road safety
management. Traditional vehicle damage inspection methods are often time-consuming, expensive, and highly
dependent on human expertise, which can result in inconsistent and subjective assessments. With the rapid
advancements in deep learning and computer vision, automated car damage detection has emerged as an effective
and reliable solution for accurate vehicle inspection.
This project presents a deep learning-based approach for Car Damage Detection using YOLOv8 (You Only Look
Once Version 8), a state-of-the-art object detection algorithm. The proposed system is trained to detect and
localize various types of vehicle damage, including dents, scratches, cracks, and broken parts, from input images.
YOLOv8 performs real-time object detection with high accuracy by processing images in a single forward pass
through the neural network, enabling fast and efficient damage identification.
The model is trained using a labeled dataset of damaged vehicle images and evaluated using standard
performance metrics such as Precision, Recall, and Mean Average Precision (mAP). Experimental results
demonstrate that YOLOv8 provides accurate, reliable, and efficient detection of vehicle damage, making it well
suited for real-world applications such as automated insurance claim assessment, vehicle inspection, and
automobile service management. This project highlights the significant potential of deep learning and computer
vision technologies in improving the speed, accuracy, and consistency of automated car damage detection
systems.
