R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous
  Driving in the Wild
Muhammad Atif Butt Hassan Ali Adnan Qayyum Waqas Sultani Ala Al-Fuqaha Junaid Qadir
Semantic understanding of roadways is a key enabling factor for safe
autonomous driving. However, existing autonomous driving datasets provide
well-structured urban roads while ignoring unstructured roadways containing
distress, potholes, water puddles, and various kinds of road patches i.e.,
earthen, gravel etc. To this end, we introduce Road Region Segmentation dataset
(R2S100K) -- a large-scale dataset and benchmark for training and evaluation of
road segmentation in aforementioned challenging unstructured roadways. R2S100K
comprises 100K images extracted from a large and diverse set of video sequences
covering more than 1000 KM of roadways. Out of these 100K privacy respecting
images, 14,000 images have fine pixel-labeling of road regions, with 86,000
unlabeled images that can be leveraged through semi-supervised learning
methods. Alongside, we present an Efficient Data Sampling (EDS) based
self-training framework to improve learning by leveraging unlabeled data. Our
experimental results demonstrate that the proposed method significantly
improves learning methods in generalizability and reduces the labeling cost for
semantic segmentation tasks. Our benchmark will be publicly available to
facilitate future research at https://r2s100k.github.io/.