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Korea Society for Naval Science and Technology - Vol. 8, No. 2
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Journal of the KNST - Vol. 8, No. 2, pp. 109-115
Abbreviation: KNST
ISSN: 2635-4926 (Print)
Print publication date 30 Jun 2025
Received 26 Apr 2025 Revised 04 May 2025 Accepted 25 May 2025
DOI: https://doi.org/10.31818/JKNST.2025.6.8.2.109

Computer Vision-based Fishing Net Detection for Autonomous Navigation of USVs
Kyeongmo Kang*
해군 대위/해군사관학교 기계시스템공학과 조교수

무인수상정의 자율주행을 위한 컴퓨터 비전 기반 어망 탐지
강경모*
LT, ROK Navy/Assistant Professor, Dept. of Mechanical System Engineering, Republic of Korea Naval Academy
Correspondence to : *Kyeongmo Kang Dept. of Mechanical System Engineering, Republic of Korea Naval Academy 1 Jungwon-ro, Jinhae-gu, Changwon-si, Gyungsangnam-do, 51704, Republic of Korea Tel: +82-55-907-5316 E-mail: kmkang@navy.ac.kr


Ⓒ 2025 Korea Society for Naval Science & Technology
Funding Information ▼
Abstract

Unmanned Surface Vehicles (USVs) are increasingly deployed in maritime operations, but entanglement with fishing nets remains a significant challenge, causing mechanical failures and operational disruptions. This study presents an indirect detection approach that identifies fishing net buoys as reliable markers instead of detecting the nets directly. A computer vision-based system was developed to detect fishing net buoys and estimate their positions, utilizing a stereo camera and transfer learning on YOLO models. Experimental validation demonstrated high detection accuracy with fishing net buoy, achieving an mAP50 of 0.98, and the system estimated target positions within a distance error of ±12.08 % and an angular error of ±3.48 %. Based on these results, the feasibility of this approach for real-time USV fishing net avoidance is demonstrated.

초록

무인 수상정은 해양 작전에서 점점 더 많이 활용되고 있지만, 어망에 얽히는 문제는 여전히 기계적 고장과 작전 차질을 초래하는 중요한 과제로 남아 있다. 본 연구는 어망을 직접 탐지하는 대신, 어망 부표를 탐지하는 간접 탐지 방식을 제안한다. 어망 부표를 탐지하고, 그 위치를 추정하기 위한 컴퓨터 비전 기반 시스템이 스테레오 카메라와 YOLO 모델의 전이 학습을 활용하여 개발하였다. 실험 결과, mAP50 기준 0.984의 높은 탐지 성능을 보였으며, 거리 오차 ±12.08 %, 각도 오차 ±3.48 % 이내에서 표적의 위치를 추정하였다. 이러한 결과를 바탕으로, 본 접근 방식이 실시간 USV 어망 회피에 적용 가능함을 입증하였다.

Keywords: Autonomous Navigation, Computer Vision, Fishing Net Detection, Stereo Camera, Unmanned Surface Vehicle
키워드: 자율운항, 컴퓨터 비전, 어망 탐지, 스테레오 카메라, 무인수상정
Acknowledgments

본 논문은 해군사관학교 해양연구소 학술연구과제 연구비의 지원으로 수행된 연구임.

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