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RISS 인기검색어
- 검색키워드
- 저자 : 최경훈(Keong-Hun Choi) (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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최경훈(Keong-Hun Choi),하종은(Jong-Eun Ha) 제어로봇시스템학회 2021 제어·로봇·시스템학회 논문지 Vol.27 No.12
In a visual surveillance system, even the same object should exhibit different detection results depending on the surrounding environment configuration. To this end, the model for visual surveillance needs to detect an object by understanding the state of the object according to the environment on the image. In this study, for such visual surveillance, an object segmentation model applied with a transformer structure suitable for image processing was used to divide objects inside the image into foreground and background. A modified attention structure was presented for the corresponding transformer structure, and the results of object segmentation models according to the type of input data were compared.
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최경훈(Keong-Hun Choi),하종은(Jong-Eun Ha) 제어로봇시스템학회 2021 제어·로봇·시스템학회 논문지 Vol.27 No.12
Currently, research on artificial intelligence that autonomously interacts with surroundings without human management has attracted research attention in vehicle and robot-related fields. The recognition of the surrounding environment is the basis for artificial intelligence that requires interaction with the surroundings, which means that research on object detection is necessary. In general, object detection proceeds in the same way as detection and segmentation. Among them, in the case of segmentation, the size of the model is smaller, and more information can be obtained than detection using anchors. However, the inferior detection performance and generalization ability of this method for small objects has limited its further application. In this paper, a modified transformer structure with different configuration of training data from the existing label data is presented to improve the performance of segmentation.
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최경훈(Keong-Hun Choi),하종은(Jong-Eun Ha) 제어로봇시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.11
Semantic segmentation aims to assign correct class labels per pixel on an image. In particular, semantic segmentation has difficulties in particular along the boundary of objects. Recently, ELKPPNet has been proposed, which improves the performance of semantic segmentation by adding edge loss term into the conventional semantic segmentation algorithm. In extracts edge from the end of the network, which is used in the computation of loss. In this paper, we present U-Net based networks which adopt the edge loss of the ELKPPNet. Presented algorithm computes edge by additional network flow in the U-Net. Two different network structures are investigated. One computes edge at the end of decoder in encoder-decoder of the U-Net. The other computes edge from the start of decoder in U-Net. Experimental results show that integrating edge information in semantic segmentation improves performance.
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