[논문]영상 기반 Semantic Segmentation 알고리즘을 이용한 도로 추출

오행열; 전승배; 김건; 정명훈

doi:10.7848/ksgpc.2022.40.3.239

[국내논문] 영상 기반 Semantic Segmentation 알고리즘을 이용한 도로 추출
Road Extraction from Images Using Semantic Segmentation Algorithm 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.40 no.3, 2022년, pp.239 - 247

오행열 (Dept. of Civil Engineering, Chosun University) , 전승배 (Dept. of Civil Engineering, Chosun University) , 김건 (Dept. of Civil Engineering, Chosun University) , 정명훈 (Dept. of Civil Engineering, Chosun University)

초록
AI-Helper

현대에는 급속한 산업화와 인구 증가로 인해 도시들이 더욱 복잡해지고 있다. 특히 도심은 택지개발, 재건축, 철거 등으로 인해 빠르게 변화하는 지역에 해당한다. 따라서 자율주행에 필요한 정밀도로지도와 같은 다양한 목적을 위해 빠른 정보 갱신이 필요하다. 우리나라의 경우 기존 지도 제작 과정을 통해 지도를 제작하면 정확한 공간정보를 생성할 수 있으나 대상 지역이 넓은 경우 시간과 비용이 많이 든다는 한계가 있다. 지도 요소 중 하나인 도로는 인류 문명을 위한 많은 다양한 자원을 제공하는 중추이자 필수적인 수단에 해당한다. 따라서 도로 정보를 정확하고 신속하게 갱신하는 것이 중요하다. 이 목표를 달성하기 위해 본 연구는 Semantic Segmentation 알고리즘인 LinkNet, D-LinkNet 및 NL-LinkNet을 사용하여 광주광역시 도시철도 2호선 공사 현장을 촬영한 드론 정사영상에서 도로를 추출한 다음 성능이 가장 높은 모델에 하이퍼 파라미터 최적화를 적용하였다. 그 결과, 사전 훈련된 ResNet-34를 Encoder로 사용한 LinkNet 모델이 85.125 mIoU를 달성했다. 향후 연구 방향으로 최신 Semantic Segmentation 알고리즘 또는 준지도 학습 기반 Semantic Segmentation 기법을 사용하는 연구의 결과와의 비교 분석이 수행될 것이다. 본 연구의 결과는 기존 지도 갱신 프로세스의 속도를 개선하는 데 도움을 줄 수 있을 것으로 예상된다.

Abstract ▼ AI-Helper

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

주제어

표/그림 (8)

그림 Fig. 1. LinkNet architecture (Chaurasia and Culurciello, 2017)
그림 Fig. 2. D-LinkNet architecture (Zhou et al., 2018)
그림 Fig. 3. NL-LinkNet architecture (Wang et al., 2021)
그림 Fig. 4. Example of drone image data generation (a) visualization of pre-processed drone image, (b) visualization of road coordinate information in image
표 Table 1. Drone image details
그림 Fig. 5. Comparison of each model inference result (a) Drone image, (b) NL-LinkNet(Resnet-18), (c) D-LinkNet(ResNet-18), (d) LinkNet(ResNet-18), (e) NL-LinkNet(ResNet-34), (f) D-LinkNet(ResNet-34), (g) LinkNet(ResNet-34), (h) Ground Truth
그림 Fig. 6. Comparison of inference results (a) drone image source, (b) ground truth, (c) LinkNet(ResNet-34) before hyperparameter optimization, and (d) LinkNet(ResNet-34) after hyperparameter optimization
표 Table 2. Model performance assessment results

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표제어: PCR

동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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