Solving Scene Understanding for Autonomous Navigation in Unstructured Environments

TL;DR

This paper evaluates semantic segmentation models on a challenging Indian driving dataset to enhance scene understanding for autonomous vehicles in unstructured environments, achieving a maximum MIOU of 0.6496.

Contribution

It introduces a new Indian driving dataset for semantic segmentation in unstructured environments and compares five deep learning models on this dataset.

Findings

DeepLabV3 achieved the highest MIOU of 0.6496.

The dataset is more challenging than Cityscapes due to unstructured environments.

Model performance varies significantly across different architectures.

Abstract

Autonomous vehicles are the next revolution in the automobile industry and they are expected to revolutionize the future of transportation. Understanding the scenario in which the autonomous vehicle will operate is critical for its competent functioning. Deep Learning has played a massive role in the progress that has been made till date. Semantic Segmentation, the process of annotating every pixel of an image with an object class, is one crucial part of this scene comprehension using Deep Learning. It is especially useful in Autonomous Driving Research as it requires comprehension of drivable and non-drivable areas, roadside objects and the like. In this paper semantic segmentation has been performed on the Indian Driving Dataset which has been recently compiled on the urban and rural roads of Bengaluru and Hyderabad. This dataset is more challenging compared to other datasets like Cityscapes, since it is based on unstructured driving environments. It has a four level hierarchy and in this paper segmentation has been performed on the first level. Five different models have been trained and their performance has been compared using the Mean Intersection over Union. These are UNET, UNET+RESNET50, DeepLabsV3, PSPNet and SegNet. The highest MIOU of 0.6496 has been achieved. The paper discusses the dataset, exploratory data analysis, preparation, implementation of the five models and studies the performance and compares the results achieved in the process.