TY  - JOUR
T1  - 해안지역의 해무현상으로 인한 저시정 현상 개선 및 미확인 물체 탐지 방안에 대한 정확성 개선 방안
AU  - Jung, Min uk 
AU  - Yoon, Soo Yeon 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.4.637
KW  - Object Detection
KW  - Deeplearning
KW  - USO detection
KW  - Background Segmentation
KW  - Real Time Processing Capability
KW  - Dehaze
KW  - Coastal Boundary Operations
AB  - South Korea, being in a tense situation with the North and surrounded by the sea on three sides due to its geographic characteristics, places great importance on coastal surveillance for national security. However, challenges arise in coastal surveillance operations due to outdated military equipment and a reduction in military personnel caused by low birth rates. Against this backdrop, this paper presents deep learning based automation technology as a substitute for human resources. Coastal regions often experience low visibility due to sea fog caused by unique climate conditions. To address this issue, the Dehazy algorithm is introduced for fog removal, and an algorithm for background separation is implemented by dividing the boundary between the sea, sky, and obstacles based on the horizon to focus on objects on the sea. For object detection, the YOLO algorithm is used, and this paper highlights the difference in object recognition rates and real time processing speed when identifying unidentified objects, both in the original and processed images.