Material Classification Using Optical Wireless Communications Data and Machine Learning

This study proposes the integration of Optical Wireless Communication (OWC) and the classification of the material type of the object whose distance to the laser is being measured, using Machine Learning (ML) techniques such as KNN, RF, and SVM. The application relies on using the OWC dynamic communication structure between vehicles to estimate relevant information in order to improve system communication and navigation. The aim of this work is to classify materials such as glass, plastic and aluminum in order to improve the functionality of the OWC system by expanding the sensory information without adding new hardware, taking advantage of the structure of the communication system already in use. The methodology employs different ML techniques, along with approaches for dealing with limited and unbalanced amounts of data, k-fold and SMOTE, in order to perform a comparative analysis and obtain an efficient classification model. The most significant impact of this study lies in offering an integrated solution that optimizes optical communication while providing material classification for selective information processing. This approach offers a comparative analysis of the ML techniques applied, obtaining an accuracy of 93% using KNN and SMOTE.