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Cart Path Recognition in a Golf Course Using Deep Fully Convolutional Networks

Title
Cart Path Recognition in a Golf Course Using Deep Fully Convolutional Networks
Authors
Son, Guk-JinKim, JunkangJung, WooyoungKim, Youngduk
DGIST Authors
Son, Guk-Jin; Kim, Junkang; Jung, WooyoungKim, Youngduk
Issue Date
2019-11-29
Citation
CANDAR 2019 : The Seventh International Symposium on Computing and Networking, 46-47
Type
Conference
ISSN
2186-5140
Abstract
Autonomous driving vehicles are a growing reality. Autonomous driving related industries are also growing. We believe that an unmanned golf cart is an appropriate application area for such autonomous driving. So, we have commenced the project of the golf cart the purpose of which is to provide an assistant to golf players. The golf cart includes almost all the functions the Autonomous driving should have. Image processing and recognition capability are of course one of the key functions that a golf cart needs. To develop such a kind of a golf cart would take much time. Thus, we have started the research and development of a demonstration prototype, which can perform some of the main tasks of the golf cart. In this paper, we will deal with the recognition of a cart path by themselves, trained end-to-end, pixels-to-pixels. We have collected and annotated the cart path of challenging scenes captured in a golf course. And we developed a deep learning network for a cart path. As a result, we achieve 93% accuracy and 50ms inference time.
URI
http://hdl.handle.net/20.500.11750/14116
Publisher
CANDAR
Related Researcher
  • Author Jung, Wooyoung  
  • Research Interests Artificial Intelligence, Machine Learning, Autonomous Driving
Files:
There are no files associated with this item.
Collection:
Division of Automotive Technology2. Conference Papers


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