Tire Wear Assessment Using VGG16: A Computer Vision Approach

This paper addresses the issue of tire bursts caused by uneven pressure and wear by implementing an efficient Computer Vision-Based tire health monitoring system using VGG16 in Machine Learning. This system involves capturing tire images using a camera and processing them with a machine learning module, such as VGG 16, to obtain the tire's current state. The system can offer early warning of potential tire failures, allowing drivers to take proactive measures to prevent accidents and minimize maintenance costs. Furthermore, it can assist fleet managers in scheduling tire replacements more efficiently, resulting in improved fleet efficiency overall. The system operates in real-time, generating results almost instantly, and can be integrated with existing vehicle systems for ease of use. The proposed tire health monitoring system is an essential solution to preventing tire bursts, enhancing safety, and minimizing maintenance costs.