Loss function for ambiguous boundaries for deep neural network (DNN) for image segmentation

被引：1

作者：

Hakumura, Yuma ^{[1
]}

Ito, Taiyo ^{[1
]}

Matsui, Shiori ^{[1
]}

Akiba, Yuya ^{[1
]}

Aoki, Kimiya ^{[1
]}

Nakashima, Yuki ^{[2
]}

Hirao, Kiyoshi ^{[2
]}

Fukushima, Manabu ^{[2
]}

机构：

[1] Chukyo Univ, 101-2 Yagotohonmachi,Showa Ku, Nagoya, Aichi 4668666, Japan

[2] Natl Inst Adv Ind Sci & Technol, Multimat Res Inst, Ceram Microstruct Control Grp, Nagoya, Aichi, Japan

来源：

ELECTRONICS AND COMMUNICATIONS IN JAPAN | 2023年 / 106卷 / 04期

关键词：

blurriness of grain boundaries; deep neural network; fine ceramics; loss function; segmentation;

D O I：

10.1002/ecj.12429

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This study deals with the task of segmentation of SEM images of fine ceramics sintered bodies by using deep neural network (DNN). In particular, we focus on misclassification caused by the blurriness of grain boundaries(boundaries between particles). Therefore, we utilize the frequency distribution of brightness gradient of grain boundaries and give higher weights to pixels with lower gradient values. Experiments confirmed that the model trained with proposed loss function gave the best prediction results.

引用

页数：9

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