Enhancing Transportation Safety with YOLO-Based CNN Autonomous Vehicles

Our project, titled Enhancing Transportation Safety with YOLO-based CNN Autonomous Vehicles”, pioneers a transformative approach to autonomous driving. Through the fusion of advanced machine learning techniques, specifically the YOLO-based Convolutional Neural Network (CNN) algorithm, with meticulously selected hardware components, including Raspberry Pi 4B and IR sensors, our system addresses critical objectives such as object recognition, lane detection, and traffic signal and signboard detection, pothole detection. By leveraging diverse image datasets and optimizing the YOLO-based CNN algorithm, our system achieves exceptional accuracy and efficiency in environmental perception and navigation. Key features such as real-time object detection and precise lane detection contribute to safe navigation through complex traffic scenarios. With potential applications across automotive, logistics, and transportation industries, our project represents a significant advancement in autonomous driving technology. By prioritizing safety and efficiency, our YOLO-based CNN autonomous vehicle system holds promise for revolutionizing transportation systems and enhancing road safety worldwide.