• Title/Summary/Keyword: 해무제거

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해무 제거 학습을 위한 가상 해무 데이터셋 생성 및 유효성 검증 연구

  • 전영수;김현철;이상훈;오세웅;옥수열
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2022.06a
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    • pp.103-105
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    • 2022
  • 인공지능을 기반으로 한 안개를 제거하는 기술은 많은 연구가 있다. 하지만 대부분의 연구가 육상을 타겟으로 하고 있기 때문에 해상에 발생하는 해무를 제거하기 위한 데이터 셋은 현저히 부족하다. 이를 해결하기 위해 가상의 해무를 생성하여 데이터 셋을 생성하고 유효성 검증을 하는 방법에 대하여 연구하였다.

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Digital Image based Real-time Sea Fog Removal Technique using GPU (GPU를 이용한 영상기반 고속 해무제거 기술)

  • Choi, Woon-sik;Lee, Yoon-hyuk;Seo, Young-ho;Choi, Hyun-jun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.20 no.12
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    • pp.2355-2362
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    • 2016
  • Seg fog removal is an important issue concerned by both computer vision and image processing. Sea fog or haze removal is widely used in lots of fields, such as automatic control system, CCTV, and image recognition. Color image dehazing techniques have been extensively studied, and expecially the dark channel prior(DCP) technique has been widely used. This paper propose a fast and efficient image prior - dark channel prior to remove seg-fog from a single digital image based on the GPU. We implement the basic parallel program and then optimize it to obtain performance acceleration with more than 250 times. While paralleling and the optimizing the algorithm, we improve some parts of the original serial program or basic parallel program according to the characteristics of several steps. The proposed GPU programming algorithm and implementation results may be used with advantages as pre-processing in many systems, such as safe navigation for ship, topographical survey, intelligent vehicles, etc.

Sea-fog Dehazing Technique base on GPU for CCTV Monitoring and Controlling System (CCTV 관제시스템을 위한 GPU 기반 해무제거 기술)

  • Kim, eun-soo;Lee, youn-hyuk;Seo, young-ho;Choi, hyun-jun
    • Proceedings of the Korea Contents Association Conference
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    • 2017.05a
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    • pp.461-462
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    • 2017
  • 본 논문에서는 해양관제 시스템을 위한 해무(sea-fog) 제거 기술을 제안한다. 이 기술은 실시간 동작을 위해GPU(graphic processing unit)를 기반으로 운용이 된다. 구현에 사용된 GPU는 nVidia사의 GTX 680으로 두 대의 GPU를 사용하여 실시간 동작을 확인하였다.

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Technique of Sea-fog Removal base on GPU (GPU 기반의 해무제거 기술)

  • Choi, Woonsik;Ha, Jun;Youn, Woosang;Kwak, Jaemin;Choi, Hyunjun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2015.05a
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    • pp.576-578
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    • 2015
  • This paper propose the help of the secure a clear view and safe navigation of the coastal ship through the sea-fog removal algorithm. Interest in marine accidents and vessel safety has increased in recent Sewol ferry event. According to statistics coastal ship cause of the marine accident when sea fog on the sea did not secure clear view the ship's occur several incidents of collisions between ships and can see that accounts for a high percentage. Algorithm for image exist sea fog is number of studies. but, such studies take up a lot of calculation quantity in the course of performing the algorithm. In this paper, we improve the computational speed of sea fog over the GPU-based technique was removed to suit real-time video. Furthermore, by using GPU, we succeeded in accelerating the simulation 250 times.

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Acceleration for Removing Sea-fog using Graphic Processors and Parallel Processing (그래픽 프로세서를 이용한 병렬연산 기반 해무 제거 고속화)

  • Kim, Young-doo;Kwak, Jae-min;Seo, Young-ho;Choi, Hyun-jun
    • Journal of Advanced Navigation Technology
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    • v.21 no.5
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    • pp.485-490
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    • 2017
  • In this paper, we propose a technique for high speed removal of sea-fog using a graphic processor. This technique uses a host processor(CPU) and several graphics processors(GPU) capable of parallel processing to remove sea-fog from the input image. In the process of removing sea-fog, the dark channel extraction, the maximum brightness channel extraction, and the calculation of the transmission are performed by the host processor, and the process of refining the transmission by applying the bidirectional filter is performed in parallel through the graphic processor. To verify the proposed parallel processing method, three NVIDIA GTX 1070 GPUs were used to construct the verification environment. As a result, it takes about 140ms when implemented with one graphics processor, and 26ms when implemented using OpenMP and multiple GPGPUs. The proposed a parallel processing algorithm based on the graphics processor unit can be used for safe navigation, port control and monitoring system.