최근 산업 현장에서 자동화 시스템 도입에 대한 필요성 증가로 인하여 물체 인식에 대한 기술의 활용도가 점차 증가되고 있다. 그러나, 실제 현장에서 조명의 영향은 물체 주변에 잡음이나 그림자를 발생시켜 물체 영역을 정확히 검출하거나 인식하는 것을 어렵게 만든다. 본 논문에서는 조명 영향으로 나타나는 잡음이나 그림자 효과를 최소화하기 위하여 영상 필터와 적응적 이진화 방법을 이용하여 물체의 형태 정보가 보존된 물체 영역을 검출하도록 하였다. 또한, 인식 대상 물체의 종류와 회전각에 따라 물체 고유 클래스를 정의한 다음 신경망을 이용하여 물체를 인식함으로써 회전에 강인한 물체 인식을 할 수 있도록 하였다. 제안된 물체 인식 방법에 대한 타당성을 검증하기 위하여 조명 조건을 달리하면서 획득한 ETRI 데이터베이스 16,848장을 대상으로 인식 실험해 본 결과 99.86%의 물체 인식률 및 0.03초의 인식 속도를 얻을 수 있었다.
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