人工智能深度学习在海事监管系统的应用
Application of Deep Learning Based on Artificial Intelligence in Maritime Supervision System
张圣东 1温启锐1
作者信息
- 1. 南海航海保障中心深圳通信中心,广东 深圳 518000
- 折叠
摘要
水上事故和灾难不仅给人们的生命财产带来严重威胁,也对海洋环境造成了严重破坏.通过采用人工智能深度学习法,使用YOLOv8目标检测技术,结合摄像头开发了一套针对漂浮蚝排与其他海上相关要素的海事监管系统,最终得到模型的准确率为91.7%.同时讨论深度学习在海事监管系统的局限性,结果表明深度学习应用于海事监管系统切实有效.
Abstract
Maritime accidents and disasters not only pose a grave threat to people's lives and property but also cause severe harm to the marine environment.This paper introduces a maritime supervision system for floating oyster rafts and other sea-related elements using the deep learning method of artificial intelligence(AI)and the YOLOv8 object detection technology with the camera.The accuracy of the final model is 91.7%.It also studies the limitations of deep learning when it is applied in the maritime administrative system.The results show that the application of deep learning in the maritime supervision system is an effective approach.
关键词
深度学习/海上危险物/智能识别/海事监管/YOLOv8/目标检测Key words
deep learning/maritime hazardous goods/intelligent identification/maritime supervision/YOLOv8/target detection引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据