Semantic Segmentation based Scene Understanding in Autonomous Vehicles

TL;DR

This paper explores various deep learning models for semantic segmentation to improve scene understanding in autonomous vehicles, emphasizing the impact of backbone selection on performance using the BDD100k dataset.

Contribution

It introduces multiple efficient models with different backbones for semantic segmentation, demonstrating their effects on accuracy and scene understanding in autonomous driving.

Findings

Backbone choice significantly affects segmentation performance.

Proposed models improve accuracy and mean IoU.

Enhanced scene understanding benefits autonomous vehicle safety.

Abstract

In recent years, the concept of artificial intelligence (AI) has become a prominent keyword because it is promising in solving complex tasks. The need for human expertise in specific areas may no longer be needed because machines have achieved successful results using artificial intelligence and can make the right decisions in critical situations. This process is possible with the help of deep learning (DL), one of the most popular artificial intelligence technologies. One of the areas in which the use of DL is used is in the development of self-driving cars, which is very effective and important. In this work, we propose several efficient models to investigate scene understanding through semantic segmentation. We use the BDD100k dataset to investigate these models. Another contribution of this work is the usage of several Backbones as encoders for models. The obtained results show that choosing the appropriate backbone has a great effect on the performance of the model for semantic segmentation. Better performance in semantic segmentation allows us to understand better the scene and the environment around the agent. In the end, we analyze and evaluate the proposed models in terms of accuracy, mean IoU, and loss function, and the results show that these metrics are improved.