본 연구에서는 광 무선통신(Optical Wireless Communication, OWC) 시스템에서 터미널 간 자동 광 정렬 및 추적 가능한 이미징 시스템을 개발하였으며, 특히, 원거리(1km 이상)에 위치한 터미널 추적 시 화질 저하로 인한 검출 성능 한계를 극복하기 위해 다양한 영상개선 알고리즘 적용하고 성능을 비교하였다. 이를 위해 OWC 터미널 추적용 타겟 이미지에 대한 학습데이터셋을 구축하고, 전이 학습을 통해 딥러닝 학습모델을 생성하였다. 현장 적용 실험을 통해 수집된 총 40장의 이미지셋에 대해, 영상개선 알고리즘 적용 전 검출 성능(mAP)는 90.0%이었으나, CLAHE(95%), FM(100%), MSR(100%), MSRCR(100%), AMSR(100%), DehazeNet(92.5%) 알고리즘 적용시 타겟 검출 성능 향상 효과를 확인하였다. 그리고, 타겟 검출 성능과 처리 속도를 고려했을 때, OWC 터미널의 coarse tracking에는 CLAHE를 적용하는 것이 최적인 것으로 분석되었다.
KSP Keywords
Optical Wireless Communications(OWC)
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