Motorcycle Recognition System Using Convolutional Neural Network

Vehicle detection is becoming increasingly important for highway management, particularly in Aklan, where motorcycles and tricycles are the predominant modes of transportation. Due to their diverse designs, accurate detection of these vehicles remains challenging. This study addresses this issue by developing a vision-based motorcycle recognition system using a Convolutional Neural Network (CNN) implemented in MATLAB. A new high-definition dataset, comprising 34,002 annotated instances from 17,785 motorcycle images and 16,217 tricycle images, was created. The dataset was collected from various locations around Ibajay, Aklan, using regular cameras and smartphones. The images were classified into motorcycles and tricycles and then used to train the CNN model, which was compared with the You Only Look Once (YOLO) version 2 network. Experimental results indicate that the proposed CNN-based motorcycle recognition system achieves high detection accuracy, comparable to the YOLOv2 model. This system contributes to Sustainable Development Goal (SDG) 11 by improving sustainable urban transportation systems and traffic management. Additionally, it holds significant commercial value for motorcycle brands through enhanced vehicle tracking and market analysis.