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GAN-based Video Denoising for Robust Pig Detection System

GAN 기반의 영상 잡음에 강인한 돼지 탐지 시스템

  • Bo, Zhao (Dept. of Computer Information Science, Korea University) ;
  • Lee, Jonguk (Dept. of Computer Convergence Software, Korea University) ;
  • Atif, Othmane (Dept. of Computer Information Science, Korea University) ;
  • Park, Daihee (Dept. of Computer Convergence Software, Korea University) ;
  • Chung, Yongwha (Dept. of Computer Convergence Software, Korea University)
  • 박철 (고려대학교 컴퓨터정보학과) ;
  • 이종욱 (고려대학교 컴퓨터융합소프트웨어학과) ;
  • 오스만 (고려대학교 컴퓨터정보학과) ;
  • 박대희 (고려대학교 컴퓨터융합소프트웨어학과) ;
  • 정용화 (고려대학교 컴퓨터융합소프트웨어학과)
  • Published : 2021.11.04

Abstract

Infrared cameras are widely used in recent research for automatic monitoring the abnormal behaviors of the pig. However, when deployed in real pig farms, infrared cameras always get polluted due to the harsh environment of pig farms which negatively affects the performance of pig monitoring. In this paper, we propose a real-time noise-robust infrared camera-based pig automatic monitoring system to improve the robustness of pigs' automatic monitoring in real pig farms. The proposed system first uses a preprocessor with a U-Net architecture that was trained as a GAN generator to transform the noisy images into clean images, then uses a YOLOv5-based detector to detect pigs. The experimental results show that with adding the preprocessing step, the average pig detection precision improved greatly from 0.639 to 0.759.

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Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1I1A3070835 and NRF-2021R1I1A3049475) and by BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF).