Classification Vehicle Tire Quality using Convolutional Neural Networks

Abstract

Tires are a very important component in a vehicle because they are related to driving safety. Defective tires often cause accidents ranging from minor to fatal accidents. Convolutional Neural Network (CNN) is a type of neural network that is used to detect and recognize objects in an image. CNN can imitate the image recognition system in the human visual cortex, making it suitable for identification and classification of image data. This research aims to develop and evaluate a CNN model that is able to classify vehicle tires as 'defective' or 'good'. Model uses a total of 1856 tire images from kaggle.com and is labeled 'defective' or 'good'. Dataset is split using four different data split ratios (60:40, 70:30, 80:20, and 90:10) to determine the optimal distribution that improves the generalization ability of the model. Model evaluation uses accuracy, precision and recall matrices, which are calculated based on the confusion matrix results from testing on 300 data samples. Research results show that the model achieves the best performance at a split ratio of 80:20, with an accuracy of 76.67%, precision of 77.33%, and recall of 76.32%.

Keywords

• classification
• vehicle tires
• convolutional neural network