Through the process of chemical vapor deposition, Tungsten Hexafluoride (WF6) is widely used by the semiconductor industry to form tungsten films. Tungsten Hexafluoride (WF6) is produced through manufacturing processes such as pulverization, wet smelting, calcination and reduction of tungsten ores. The manufacturing process of Tungsten Hexafluoride (WF6) is required thorough quality control to improve productivity. In this paper, a real-time detection system for oxidation defects that occur in the manufacturing process of Tungsten Hexafluoride (WF6) is proposed. The proposed system is implemented by applying YOLOv5 based on Convolutional Neural Network (CNN); it is expected to enable more stable management than existing management, which relies on skilled workers. The implementation method of the proposed system and the results of performance comparison are presented to prove the feasibility of the method for improving the efficiency of the WF6 manufacturing process in this paper. The proposed system applying YOLOv5s, which is the most suitable material in the actual production environment, demonstrates high accuracy (mAP@0.5 99.4 %) and real-time detection speed (FPS 46).

1. 서 론
2. 이 론
    2.1. 객체 탐지 알고리즘 관련 연구
    2.2. 육불화텅스텐(WF6) 제조 공정
3. 실험 방법
4. 결과 및 고찰
5. 결 론
References

• 손승용(경북대학교 산업대학원) | Seung-Yong Son (Graduate School of Industry, Kyungpook National University, Daegu 41566, Republic of Korea)
• 김영목(국방기술품질원) | Young Mok Kim (Defense Agency for Technology and Quality, Daejeon 34327, Republic of Korea)
• 최두현(경북대학교 전자공학부) | Doo-Hyun Choi (School of Electronics Engineering, Kyungpook National University, Daegu 41566, Republic of Korea) Corresponding author