객체 검출은 예상치 못한 위험 상황을 회피할 수 있도록 작업자에게 자동으로 경보를 제공할 수 있는 중요한 산업 안전 기술 중 하나이다. 그러나 딥 러닝 기반 객체 감지 모델은 더 높은 성능을 달성하기 위해 대규모의 학습 데이터가 요구되며, 데이터 수집 및 레이블링 작업은 인력이 필요한 힘든 작업이다. 이러한 한계를 해결하기 위해, 본 논문에서는 본래의 데이터세트를 더욱 다양한 데이터로 보충할 수 있는 GAN 기반 데이터 증강을 제안한다. 또한, 생성된 데이터의 충실도를 향상시키기 위한 트랜스포머 기반 생성자 네트워크를제시하며, 비교 연구를 위해 다양한 증강 설정에서 훈련된 기존 객체 감지 모델(YOLOv5)을 평가한다. 평가결과는 20% 증강된 데이터로 학습한 모델이 객체 특정 성능의 손실 없이 0.9%의 분류 능력이 향상되었음을보여준다.
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