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http://dx.doi.org/10.9766/KIMST.2022.25.6.606

A Study of Real-time Semantic Segmentation Performance Improvement in Unstructured Outdoor Environment  

Daeyoung, Kim (Department of Electrical and Computer Engineering, Seoul National University)
Seunguk, Ahn (Department of Defense Robotics and Autonomous Systems Development, Hanwha Defense Co., Ltd.)
Seung-Woo, Seo (Department of Electrical and Computer Engineering, Seoul National University)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.25, no.6, 2022 , pp. 606-616 More about this Journal
Abstract
Semantic segmentation in autonomous driving for unstructured environments is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures. Current off-road datasets exhibit difficulties like class imbalance and understanding of varying environmental topography. To overcome these issues, we propose a deep learning framework for semantic segmentation that involves a pooled class semantic segmentation with five classes. The evaluation of the framework is carried out on two off-road driving datasets, RUGD and TAS500. The results show that our proposed method achieves high accuracy and real-time performance.
Keywords
Deep Learning; Semantic Segmentation; Autonomous Driving;
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