Tire wear detection for accident avoidance employing convolutional neural networks

Abstract

Tires are one of the most essential components of a vehicle, as they actively contribute to driving dynamics. However, they are often among the most overlooked when it comes to proper scrutiny and maintenance. More often than not, the general masses are found to be negligent of the condition of their tires. Treadwear and sidewall damage occur in abundance, and not tending to these problems can have devastating consequences in the long run. There is an innumerable number of road accident cases reported which were found to have been caused due to use of damaged and worn-out tires, and these occurrences are more prevalent in highways and during the rainy season. Despite being a widespread issue, many people are unable to identify good usable tires from worn-out ones, increasing their likelihood of using dangerous unsafe tires on roads. This paper introduces a model that can differentiate between good and worn-out tires, which has been implemented using Image Processing. The model takes external pictures of tires provided by the user as input and provides a verdict on its condition after comparing them with the model's dataset using the machine learning algorithms DenseNet and MobileNet. This model has been made keeping in mind that it can be further used with appropriate hardware for implementing in real-life applications. By enforcing said implementation by the concerned regulatory bodies, tire-related accidents can be sharply reduced and damage to human life and property can be prevented on public roads.