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2024 Vol.34, Issue 3 Preview Page

Original Article

A Study on Low-Light Image Enhancement Technique for Improvement of Object Detection Accuracy in Construction Site

건설현장 내 객체검출 정확도 향상을 위한 저조도 영상 강화 기법에 관한 연구

Jong-Ho Na1
,
Jun-Ho Gong2*
,
Hyu-Soung Shin3
,
Il-Dong Yun4
나 종호1
,
공 준호2*
,
신 휴성3
,
윤 일동4
1Student Researcher, Department of Future & Smart Construction Research, Korea Institute of Civil Engineering and Building Technology
2Postdoctoral Researcher, Department of Future & Smart Construction Research, Korea Institute of Civil Engineering and Building Technology
3Senior Research Fellow, Department of Future & Smart Construction Research, Korea Institute of Civil Engineering and Building Technology
4Professor, Division of Computer Engineering, Hankuk University of Foreign Studies
1한국건설기술연구원 미래스마트건설연구본부 학생연구원
2한국건설기술연구원 미래스마트건설연구본부 박사후연구원
3한국건설기술연구원 미래스마트건설연구본부 선임연구위원
4한국외국어대학교 컴퓨터공학과 교수
*Corresponding Author
30 June 2024. pp. 208-217
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Abstract

There is so much research effort for developing and implementing deep learning-based surveillance systems to manage health and safety issues in construction sites. Especially, the development of deep learning-based object detection in various environmental changes has been progressing because those affect decreasing searching performance of the model. Among the various environmental variables, the accuracy of the object detection model is significantly dropped under low illuminance, and consistent object detection accuracy cannot be secured even the model is trained using low-light images. Accordingly, there is a need of low-light enhancement to keep the performance under low illuminance. Therefore, this paper conducts a comparative study of various deep learning-based low-light image enhancement models (GLADNet, KinD, LLFlow, Zero-DCE) using the acquired construction site image data. The low-light enhanced image was visually verified, and it was quantitatively analyzed by adopting image quality evaluation metrics such as PSNR, SSIM, Delta-E. As a result of the experiment, the low-light image enhancement performance of GLADNet showed excellent results in quantitative and qualitative evaluation, and it was analyzed to be suitable as a low-light image enhancement model. If the low-light image enhancement technique is applied as an image preprocessing to the deep learning-based object detection model in the future, it is expected to secure consistent object detection performance in a low-light environment.

Keywords
Low-light image enhancement
Deep learning
Construction site
Object detection
Image processing

AI영상 기반 건설현장 안전관리 모니터링 시스템 개발 및 적용하는 추세에 다양한 환경변화에 따른 위험 객체 탐지 딥러닝 모델 개발에 많은 연구적 관심이 쏟아지고 있다. 여러 환경 변화요인 중 저조도 조건에서 객체 검출 모델의 정확도는 현저히 감소하며, 저조도 환경을 고려한 학습을 수행하더라도 일관적인 객체 탐지 정확도를 확보할 수 없다. 이에 따라 저조도 영상을 강화하는 영상 전처리 기술의 필요성이 대두된다. 따라서, 본 논문은 취득된 건설 현장 영상 데이터를 활용하여 다양한 딥러닝 기반 저조도 영상 강화 모델(GLADNet, KinD, LLFlow, Zero-DCE)을 학습하고, 모델별 저조도 영상 강화 성능을 비교 검증 실험을 진행하였다. 저조도 강화된 영상을 시각적으로 검증하였고, 영상품질 평가 지수(PSNR, SSIM, Delta-E)를 도입하여 정량적으로 분석하였다. 실험 결과, GLADNet의 저조도 영상 강화 성능이 정량·정성적 평가에서 우수한 결과를 보여줬으며, 저조도 영상 강화 모델로 적합한 것으로 분석되었다. 향후 딥러닝 기반 객체 검출 모델에 저조도 영상 강화 기법이 전처리 단계로 적용한다면, 저조도 환경에서 일관된 객체 검출 성능을 확보할 것으로 예상된다.

키워드
저조도 영상 강화
딥러닝
건설 현장
객체 인식
영상 처리
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Information
  • Publisher :Korean Society for Rock Mechanics and Rock Engineering
  • Publisher(Ko) :한국암반공학회
  • Journal Title :Tunnel and Underground Space
  • Journal Title(Ko) :터널과 지하공간
  • Volume : 34
  • No :3
  • Pages :208-217
  • Received Date : 2024-06-07
  • Revised Date : 2024-06-13
  • Accepted Date : 2024-06-14
  • DOI :https://doi.org/10.7474/TUS.2024.34.3.208
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