As the importance of artificial intelligence grows rapidly and emerges as a leader in technology, it is becoming an important variable in the next-generation industrial system along with the robot industry. In this study, a safety system was developed using deep learning technology to provide worker safety in a robot workplace environment. The implemented safety system has multiple cameras installed with various viewing directions to avoid blind spots caused by interference. Workers in various scenario situations were detected, and appropriate robot response scenarios were implemented according to the worker's risk level through IO communication. For human detection, the YOLO algorithm, which is widely used in object detection, was used, and a separate robot class was added and learned to compensate for the problem of misrecognizing the robot as a human. The performance of the implemented system was evaluated by operator detection performance by applying various operator scenarios, and it was confirmed that the safety system operated stably.

1. 서 론
    1.1 연구배경
    1.2 연구 목적 및 방법
2. 안전시스템 개요
    2.1 YOLO 알고리즘 개요
    2.2 안전시스템 개요
3. 실험 및 검증
4. 결론

• 김진배(호서대학교대학원 나노융합기술학과) | Jin-Bae Kim (Department of Nanofusion Technology at Hoseo University Graduate School)
• 권순현(호서대학교대학원 나노융합기술학과) | Sun-Hyun Kwon (Department of Nanofusion Technology at Hoseo University Graduate School)
• 이만수(호서대학교대학원 나노융합기술학과) | Man-Soo Lee (Department of Nanofusion Technology at Hoseo University Graduate School) Corresponding Author