船舶监控三维视觉图像模糊目标清晰处理研究
Research on clear processing of blurry targets in three-dimensional visual ship monitoring images
彭笛 1汪芳1
作者信息
- 1. 武汉华夏理工学院,湖北武汉 430223
- 折叠
摘要
对船舶周围环境实时监控有利于提升航行的安全性,立体视觉技术通过计算图像之间的视差来重建目标的三维结构,但是三维视觉图像的构建严重依赖于图像质量.本文对暗通道分析技术和深度神经网络技术进行研究,分析图像去雾以及通过绘制环境场景进行还原的基本流程,提出一种基于MobileNetV2的轻量级雾浓度分类器,得到了不同训练周期时图像分类的训练准确率和验证准确率,对船舶监控三维视觉图像模糊目标清晰化处理进行验证试验,结果表明清晰化处理后的环境空间图像质量得到了大幅度提升.
Abstract
Real-time monitoring of the surrounding environment is beneficial for enhancing the safety of navigation.Stereo vision technology reconstructs the three-dimensional structure of the target by calculating the disparity between im-ages.However,the construction of three-dimensional visual images heavily relies on image quality.This paper studies dark channel analysis technology and deep neural network technology,analyzes the basic process of image defogging and restora-tion,and proposes a lightweight fog density classifier based on MobileNet V2.It presents the training accuracy and valida-tion accuracy of image classification at different training epochs.Verification experiments are conducted on the clear pro-cessing of blurry targets in three-dimensional visual ship monitoring images,and the results show that the image quality has been significantly improved after the clarification treatment.
关键词
三维视觉图像/去雾/清晰化处理/暗通道分析Key words
three-dimensional visual imagery/defogging/clarification processing/dark channel analysis引用本文复制引用
基金项目
湖北省高等学校优秀中青年科技创新团队计划项目(T2018371)
出版年
2024
NETL
NSTL
万方数据