한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제26권12호
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  • Pages.1919-1925
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  • 2022
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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잔차 연결의 조건부 생성적 적대 신경망을 사용한 시맨틱 객체 분할

Semantic Object Segmentation Using Conditional Generative Adversarial Network with Residual Connections

  • Ibrahem, Hatem (School of Information and Communication Engineering, Chung-Buk National University) ;
  • Salem, Ahmed (School of Information and Communication Engineering, Chung-Buk National University) ;
  • Yagoub, Bilel (School of Information and Communication Engineering, Chung-Buk National University) ;
  • Kang, Hyun Su (School of Information and Communication Engineering, Chung-Buk National University) ;
  • Suh, Jae-Won (School of Electronics Engineering, Chung-Buk National University)
  • 투고 : 2022.11.22
  • 심사 : 2022.12.04
  • 발행 : 2022.12.31
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초록

본 논문에서는 시맨틱 분할을 위한 조건부 생성적 적대 신경망 기반의 이미지 대 이미지 변환 접근법을 제안한다. 시맨틱 분할은 동일한 개체 클래스에 속하는 이미지 부분을 함께 클러스터링하는 작업이다. 기존의 픽셀별 분류 방식과 달리 제안하는 방식은 픽셀 회귀 방식을 사용하여 입력 RGB 이미지를 해당 시맨틱 분할 마스크로 구문 분석한다. 제안하는 방법은 Pix2Pix 이미지 합성 방식을 기반으로 하였다. 잔차 연결이 훈련 프로세스를 가속화하고 더 정확한 결과를 생성하므로 생성기 및 판별기 아키텍처 모두에 대해 잔여 연결 기반 컨볼루션 신경망 아키텍처를 사용하였다. 제안하는 방법은 NYU-depthV2 데이터셋를 이용하여 학습 및 테스트 되었으며 우수한 mIOU 값(49.5%)을 달성할 수 있었다. 또한 시맨틱 객체분할 실험에서 제안한 방법과 현재 방법을 비교하여 제안한 방법이 기존의 대부분의 방법들보다 성능이 우수함을 보였다.

In this paper, we propose an image-to-image translation approach based on the conditional generative adversarial network for semantic segmentation. Semantic segmentation is the task of clustering parts of an image together which belong to the same object class. Unlike the traditional pixel-wise classification approach, the proposed method parses an input RGB image to its corresponding semantic segmentation mask using a pixel regression approach. The proposed method is based on the Pix2Pix image synthesis method. We employ residual connections-based convolutional neural network architectures for both the generator and discriminator architectures, as the residual connections speed up the training process and generate more accurate results. The proposed method has been trained and tested on the NYU-depthV2 dataset and could achieve a good mIOU value (49.5%). We also compare the proposed approach to the current methods in semantic segmentation showing that the proposed method outperforms most of those methods.

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과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1007571) and (No. 2022R1A5A8026986).

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