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RESEARCH AREAS
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Al / Deep Learning
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Deep Learning
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Deep Learning 은 어렵지만 기존에 해결하기 어려웠던 문제에 유의미한 답을 줄 수 있는 방법이 되기도 합니다.
NBsolution은 컴퓨터 비전의 어려운 문제를 해결하기 위해 Deep Learning 을 사용하고 있습니다.
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Deep Learning 은 학습과 적용 단계로 나뉩니다.
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background_position=”left top” undefined=”” background_repeat=”no-repeat” hover_type=”none” border_color=”” border_style=”solid” border_position=”all” border_radius_top_left=”” border_radius_top_right=”” border_radius_bottom_left=”” border_radius_bottom_right=”” box_shadow=”no” box_shadow_vertical=”” box_shadow_horizontal=”” box_shadow_blur=”0″ box_shadow_spread=”0″ box_shadow_color=”” box_shadow_style=”” padding_top=”0.5%” padding_right=”8%” padding_bottom=”0″ padding_left=”8%” margin_top=”” margin_bottom=”” animation_type=”” animation_direction=”left” animation_speed=”0.3″ animation_offset=”” last=”false” border_sizes_top=”0″ border_sizes_bottom=”0″ border_sizes_left=”0″ border_sizes_right=”0″ first=”true” spacing_right=”” type=”1_2″][fusion_title hide_on_mobile=”small-visibility,medium-visibility,large-visibility” class=”” id=”” content_align=”left” size=”3″ font_size=”” line_height=”” letter_spacing=”” margin_top=”40px” margin_bottom=”0px” margin_top_mobile=”” 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학습
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요구사항 분석을 통해 필요한 이미지군을 분류합니다.
학습에 필요한 이미지들을 수집합니다.
학습 조건에 맞는 이미지들을 선별해 냅니다.
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학습에 필요한 특성을 만들어냅니다.
다양한 이미지들을 일정한 형식으로 만듭니다.
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학습을 합니다.
학습 이후 검증 결과에 따라 모델을 수정하기도 합니다.
과적합이 되지 않도록 유의하며, 재학습의 반복을 통해 AI 를 향샹 시킵니다.
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학습이 끝난 결과를 검증합니다.
한 가지 모델이 아닌 여러 모델을 학습하고 비교 검증합니다.
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적용
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실 운용되는 시스템에서 이미지를 획득합니다.
학습 이미지와의 형식을 일치시키기 위해 전처리 과정이 필요할 수 있습니다.
학습에 도움이 되는 이미지라면 해당 이미지는 다시 학습을 위해 사용될 수도 있습니다.
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학습이 끝난 AI 를 통해 판단을 합니다.
서버는 매일 지속적으로 학습을 할 수도 있기 때문에 검증이 완료된 서버의 가장 최신 AI 모델을 적용할 수 있어야 합니다.
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실 운용 시스템에 결과가 반영됩니다.
운용 결과는 사용자의 화면에 실시간 반영되어 보여집니다.
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작업 화면
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동산 관리 시스템
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획득된 이미지내의 물품의 종류를 판별하는 시스템입니다.
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이미지 인식은 컴퓨터 비전에서 오랫동안 연구해 오던 주제로 CNN을 사용하였습니다.
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CNN(Convolutional Neural Network)은 이미지를 통한 사물의 판별, 인식에 주로 쓰이는 방법입니다.
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차량 번호판 인식
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획득된 이미지에서 번호판 영역을 검출하고 번호판 문자를 인식하는 시스템입니다.
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인식에 대상이 되는 물체의 경계를 검출하기 위해 R-CNN을 사용하였습니다.
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R-CNN(Regions with CNN features) , Fast R-CNN, Faster R-CNN 등은 이미지내에 판별 대상이 되는 사물의 존재여부와 해당 사물의 위치를 인식하기 위해 제안된 모델들입니다.
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사물의 경계를 찾아내는 작업(region proposal step)과 해당 사물이 무엇인지 맞추는 작업(classification step) 둘로 나뉩니다.
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