The Optical Barcode Detection and Recognition Method Based on Visible Light Communication Using Machine Learning

Abstract

Visible light communication (VLC) has developed rapidly in recent years. VLC has the advantages of high confidentiality, low cost, etc. It could be an effective way to connect online to offline (O2O). In this paper, an RGB-LED-ID detection and recognition method based on VLC using machine learning is proposed. Different from traditional encoding and decoding VLC, we develop a new VLC system with a form of modulation and recognition. We create different features for different LEDs to make it an Optical Barcode (OBC) based on a Complementary Metal-Oxide-Semiconductor (CMOS) senor and a pulse-width modulation (PWM) method. The features are extracted using image processing and then support vector machine (SVM) and artificial neural networks (ANN) are introduced into the scheme, which are employed as a classifier. The experimental results show that the proposed method can provide a huge number of unique LED-IDs with a high LED-ID recognition rate and its performance in dark and distant conditions is significantly better than traditional Quick Response (QR) codes. This is the first time the VLC is used in the field of Internet of Things (IoT) and it is an innovative application of RGB-LED to create features. Furthermore, with the development of camera technology, the number of unique LED-IDs and the maximum identifiable distance would increase. Therefore, this scheme can be used as an effective complement to QR codes in the future.

Keywords

• visible light communication (VLC)
• machine learning
• CMOS image sensor
• image processing
• RGB-LED