Deep Learning Based Real Time Semantic Segmentation of Autonomous Vehicles

This research presents an innovative navigation framework designed for autonomous vehicles. It integrates advanced deep learning models ResNet50, U-Net, and Efficient- NetBO to address real-time semantic segmentation challenges in dynamic driving environments. The framework is tailored to enhance the vehicle's capacity to interpret complex surroundings with minimal human guidance. The ResNet50 model serves as the primary feature extractor, capturing intricate spatial information from high-resolution camera feeds, which aids in accurately detecting critical elements like lane boundaries, pedestrians, and road signs. U-Net then performs pixel-level segmentation, enabling a detailed understanding of the scene by categorizing each image segment. EfficientN etBO is integrated to optimize the balance between model precision and computational speed, ensuring real-time performance essential for autonomous driving. This architecture supports robust decision-making capabilities, allowing for precise tasks such as obstacle detection, lane maintenance, and dynamic path planning, even in complex, shifting scenarios. By combining the unique strengths of these models, the framework surpasses the limitations of traditional navigation methods, improving the safety, dependability, and overall efficacy of autonomous systems. The use of advanced semantic segmentation within this approach has the potential to propel autonomous technology forward, enabling more intelligent and adaptive vehicles.