[논문]딥러닝을 이용하여 생성한 초해상화 드론 영상의 정량적 평가

서홍덕; 소형윤; 김의명

doi:10.22640/lxsiri.2023.53.2.5

[국내논문] 딥러닝을 이용하여 생성한 초해상화 드론 영상의 정량적 평가
Quantitative Evaluation of Super-resolution Drone Images Generated Using Deep Learning 원문보기

지적과 국토정보 = Journal of cadastre & land informatix, v.53 no.2, 2023년, pp.5 - 18

서홍덕 (남서울대학교 공간정보공학과) , 소형윤 (국립재난안전연구원 재난정보연구실) , 김의명 (남서울대학교 드론공간정보공학과)

초록
AI-Helper

드론과 센서의 발달이 가속화됨에 따라 드론에 장착된 다양한 센서로 취득된 데이터를 융합하여 새로운 서비스 및 가치를 창출하고 있다. 그러나 데이터 융합을 통한 공간정보 구축은 주로 영상에 의존하여 구축하며, 하드웨어의 사양 및 성능에 따라 데이터 품질이 결정된다. 또한, 고품질 공간정보를 구축하기 위해 고가 장비가 요구되므로 실제 현장에서 사용하기에는 어려운 실정이다. 본 연구에서는 드론에 장착된 RGB 및 THM 카메라를 통해 취득된 저해상도 영상을 딥러닝에 적용하여 초해상화를 수행하고, 이를 통해 생성된 고해상도 영상의 정량적 평가 및 특징점 추출에 대한 평가를 수행하였다. 실험 결과 초해상화를 수행하여 생성된 고해상도 영상은 원본 영상의 특징을 유지하고 있었으며, 해상도가 개선됨에 따라 원본 영상 대비 많은 특징을 추출할 수 있었다. 따라서, 저해상도 영상을 초해상화 딥러닝 모델에 적용하여 고해상도 영상을 생성할 경우 하드웨어에 제약을 받지 않고 고품질의 공간정보를 구축하기 위한 새로운 방법일 것으로 판단하였다.

Abstract ▼ AI-Helper

As the development of drones and sensors accelerates, new services and values are created by fusing data acquired from various sensors mounted on drone. However, the construction of spatial information through data fusion is mainly constructed depending on the image, and the quality of data is determined according to the specification and performance of the hardware. In addition, it is difficult to utilize it in the actual field because expensive equipment is required to construct spatial information of high-quality. In this study, super-resolution was performed by applying deep learning to low-resolution images acquired through RGB and THM cameras mounted on a drone, and quantitative evaluation and feature point extraction were performed on the generated high-resolution images. As a result of the experiment, the high-resolution image generated by super-resolution was maintained the characteristics of the original image, and as the resolution was improved, more features could be extracted compared to the original image. Therefore, when generating a high-resolution image by applying a low-resolution image to an super-resolution deep learning model, it is judged to be a new method to construct spatial information of high-quality without being restricted by hardware.

Keyword

표/그림 (15)

그림 Figure 1. SRCNN and FSRCNN architectures
그림 Figure 2. RGB and THM images
그림 Figure 3. RGB and THM images with gaussian filters applied
표 Table 1. Number of data
표 Table 2. Parameters for training
그림 Figure 4. RGB image with SRCNN applied
그림 Figure 5. THM image with SRCNN applied
그림 Figure 6. RGB image with FSRCNN applied
그림 Figure 7. THM image with SRCNN applied
표 Table 3. Quantitative evaluation of RGB image
그림 Figure 8. Feature extraction result of RGB image
표 Table 4. Quantitative evaluation of THM image
그림 Figure 9. Feature extraction result of THM image
표 Table 5. Number of feature points extracted from RGB image
표 Table 6. Number of feature points extracted from THM image

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