Presentation 2021-03-05
Semantic Segmentation based on MobileNet Extended with FPN
Yuki Sugimoto, Masaki Aono,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Semantic Segmentation is attracting attention in autonomous driving, but high-precision models require a huge amount of calculations for segmentation. Therefore, for the purpose of reducing the weight of the model, we propose an architecture in which MobileNet, which is a lightweight and highly accurate CNN, is used for the Backbone Module and RFPN (Reshaped Feature Pyramid Network) is added between the Backbone Module and the Head Module. In order to realize a model with excellent accuracy and FLOPs(FLoating-point OPerations), we conducted experiments in which various parameters of RFPN were changed. As a result, it was shown that the proposed method can output with relatively high accuracy with a small FLOPs.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Semantic Segmentation / 深層学習
Paper # PRMU2020-91
Date of Issue 2021-02-25 (PRMU)

Conference Information
Committee PRMU / IPSJ-CVIM
Conference Date 2021/3/4(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English) Computer Vision and Pattern Recognition for specific environment
Chair Yoichi Sato(Univ. of Tokyo)
Vice Chair Akisato Kimura(NTT) / Masakazu Iwamura(Osaka Pref. Univ.)
Secretary Akisato Kimura(Mobility Technologies) / Masakazu Iwamura(Chubu Univ.)
Assistant Takashi Shibata(NTT) / Masashi Nishiyama(Tottori Univ.)

Paper Information
Registration To Technical Committee on Pattern Recognition and Media Understanding / Special Interest Group on Computer Vision and Image Media
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Semantic Segmentation based on MobileNet Extended with FPN
Sub Title (in English)
Keyword(1) Semantic Segmentation
Keyword(2) 深層学習
1st Author's Name Yuki Sugimoto
1st Author's Affiliation Toyohashi University of Technology(TUT)
2nd Author's Name Masaki Aono
2nd Author's Affiliation Toyohashi University of Technology(TUT)
Date 2021-03-05
Paper # PRMU2020-91
Volume (vol) vol.120
Number (no) PRMU-409
Page pp.pp.127-132(PRMU),
#Pages 6
Date of Issue 2021-02-25 (PRMU)