Deep Learning based Screen Display Fault Detection System for Vehicle Infotainment Applications

Abstract

Modern vehicles are integrated with in-vehicle infotainment systems and are subject to software faults. This paper explores the application of deep learning algorithms to identify visual defects in infotainment systems and automatically document the issues. A real-time capable framework is deployed, delivering immediate feedback on detected defects. The proposed system performs thorough analysis, automatically summarizes detected defects, and generates detailed reports, significantly reducing manual documentation effort and supporting faster decision-making. The performance of the developed models is evaluated using Convolutional Neural Networks (CNN) and Artificial Neural Network (ANN) classifiers. Experimental results demonstrate the superior performance of the CNN model, achieving a training accuracy of 82.21% with an F1 score of 0.85, and a testing accuracy of 80.51% with an F1 score of 0.811. In comparison, the ANN model achieves a training accuracy of 70.18% with an F1 score of 0.7314, and a testing accuracy of 69.32% with an F1 score of 0.705.