- Patent Title: Learning method and learning device for improving segmentation performance in road obstacle detection required to satisfy level 4 and level 5 of autonomous vehicles using laplacian pyramid network and testing method and testing device using the same
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Application No.: US16257713Application Date: 2019-01-25
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Publication No.: US10402977B1Publication Date: 2019-09-03
- Inventor: Kye-Hyeon Kim , Yongjoong Kim , Insu Kim , Hak-Kyoung Kim , Woonhyun Nam , SukHoon Boo , Myungchul Sung , Donghun Yeo , Wooju Ryu , Taewoong Jang , Kyungjoong Jeong , Hongmo Je , Hojin Cho
- Applicant: Stradvision, Inc.
- Applicant Address: KR Pohang, Gyeongbuk
- Assignee: Stradvision, Inc.
- Current Assignee: Stradvision, Inc.
- Current Assignee Address: KR Pohang, Gyeongbuk
- Agency: Kaplan Breyer Schwarz, LLP
- Main IPC: G06T7/11
- IPC: G06T7/11 ; G06K9/62 ; G06N3/08 ; G06T7/13 ; G06N20/00 ; G06T3/40 ; G06K9/00

Abstract:
A learning method for improving a segmentation performance in detecting edges of road obstacles and traffic signs, etc. required to satisfy level 4 and level 5 of autonomous vehicles using a learning device is provided. The traffic signs, as well as landmarks and road markers may be detected more accurately by reinforcing text parts as edge parts in an image. The method includes steps of: the learning device (a) instructing k convolutional layers to generate k encoded feature maps, including h encoded feature maps corresponding to h mask layers; (b) instructing k deconvolutional layers to generate k decoded feature maps (i) by using h bandpass feature maps and h decoded feature maps corresponding to the h mask layers and (ii) by using feature maps to be inputted respectively to k-h deconvolutional layers; and (c) adjusting parameters of the deconvolutional and convolutional layers.
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