  The real-time segmentation of drivable areas plays a vital role in
accomplishing autonomous perception in cars. Recently there have been some
rapid strides in the development of image segmentation models using deep
learning. However, most of the advancements have been made in model
architecture design. In solving any supervised deep learning problem related to
segmentation, the success of the model that one builds depends upon the amount
and quality of input training data we use for that model. This data should
contain well-annotated varied images for better working of the segmentation
model. Issues like this pertaining to annotations in a dataset can lead the
model to conclude with overwhelming Type I and II errors in testing and
validation, causing malicious issues when trying to tackle real world problems.
To address this problem and to make our model more accurate, dynamic, and
robust, data augmentation comes into usage as it helps in expanding our sample
training data and making it better and more diversified overall. Hence, in our
study, we focus on investigating the benefits of data augmentation by analyzing
pre-existing image datasets and performing augmentations accordingly. Our
results show that the performance and robustness of existing state of the art
(or SOTA) models can be increased dramatically without any increase in model
complexity or inference time. The augmentations decided on and used in this
paper were decided only after thorough research of several other augmentation
methodologies and strategies and their corresponding effects that are in
widespread usage today. All our results are being reported on the widely used
Cityscapes Dataset.
