KSC 2017
Current Result Document :
| 한글제목(Korean Title) |
Multiple Dilation과 Feature Summation을 이용한 객체 검출 백본 네트워크 |
| 영문제목(English Title) |
Backbone Network for Objects Detection with Multiple Dilation and Feature Summation |
| 저자(Author) |
쿤조노 바니 나탈리아
고승현
방양
조근식
Vani Natalia Kuntjono
Seung-hyun Ko
Yang Fang
Geun-Sik Jo
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| 원문수록처(Citation) |
VOL 44 NO. 02 PP. 0763 ~ 0765 (2017. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The advancement of deep learning leads to the trend of using very deep network. But, it is not practical in real project, especially for people who have limited resources or real-time requirement. In this paper, we propose a new backbone network for object detection combined with multiple dilation and feature summation. By using feature summation, we can prevent loss of spatial information that is caused by convolving. And we can widen the receptive field of individual neurons without adding more parameters by using multiple dilated convolution. And by using shallow neural network as backbone network, our network can be trained and used in environment with limited resources and without pretraining it in ImageNet dataset. Based on our experiment, our network got 71% accuracy.
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| 키워드(Keyword) |
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