Investigation of Deep Learning Based Semantic Segmentation Models for Autonomous Vehicles

Semantic segmentation plays a pivotal role in enhancing the perception capabilities of autonomous vehicles and self-driving cars, enabling them to comprehend and navigate complex real-world environments. Numerous techniques have been developed to achieve semantic segmentation. Still, the paper emphasizes the effectiveness of deep learning approaches because they have demonstrated impressive capabilities in capturing intricate patterns and features from images, resulting in highly accurate segmentation results. Although various studies have been conducted in literature, there is needed for a careful investigation and analysis of the existing methods, especially in terms of two critical aspects: accuracy and inference time. To address this need for analysis and investigation, the research focuses on three widely-used deep learning architectures: ResNet, VGG, and MobileNet. By thoroughly evaluating these models based on accuracy and inference time, the study aims to identify the models that strike the best balance between precision and speed. The findings of this study highlight the most accurate and efficient models for semantic segmentation, aiding the development of reliable self-driving technology.