Semantic Road Segmentation using Deep Learning
Tuan Van Pham
Abstract
Semantic segmentation is an important task in self-driving cars. The aims of semantic segmentation are to recognize pre-defined objects and its pixel-by-pixel location. The most popular method in semantic segmentation is Deep learning which has considerably improved semantic image segmentation. This work does an overview for semantic segmentation using Deep learning. This works also implement comparisons in term of precision, mean IOU and processing time. Three popular algorithms are PSPNet, FCN and SegNet that are examined carefully. In detail, the aim of this work points out a trade-off between processing time and mean IOU, and also between processing time and precision. Moreover, this paper concentrates on road segmentation for embedded devices, so processing time is significantly important. This work also figures out which method is suitable for embedded devices on road segmentation.