Paved and Unpaved Road Segmentation Using Deep Neural Network
- Authors
- Lee, Dabeen; Kim, Seunghyun; Lee, Hongjun; Chung, Chung Choo; Kim, Whoi Yul
- Issue Date
- Mar-2020
- Publisher
- Springer
- Keywords
- Autonomous driving; Class imbalance; Road type; Semantic segmentation
- Citation
- Communications in Computer and Information Science, v.1180, pp.20 - 28
- Indexed
- SCOPUS
- Journal Title
- Communications in Computer and Information Science
- Volume
- 1180
- Start Page
- 20
- End Page
- 28
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4454
- DOI
- 10.1007/978-981-15-3651-9_3
- ISSN
- 1865-0929
- Abstract
- Semantic segmentation is essential for autonomous driving, which classifies roads and other objects in the image and provides pixel-level information. For high quality autonomous driving, it is necessary to consider the driving environment of the vehicle, and the vehicle speed should be controlled according to types of road. For this purpose, the semantic segmentation module has to classify types of road. However, current public datasets do not provide annotation data for these road types. In this paper, we propose a method to train the semantic segmentation model for classifying road types. We analyzed the problems that can occur when using a public dataset like KITTI or Cityscapes for training, and used Mapillary Vistas data as training data to get generalized performance. In addition, we use focal loss and over-sampling techniques to alleviate the class imbalance problem caused by relatively small class data.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
- 서울 공과대학 > 서울 전기공학전공 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.