基于卷积神经网络和线性加权决策融合的海上船舶识别,用于多式联络图像
Yongmei Ren1, Xiaohu Wang2, Jie Yang3
1School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang 421002, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
本研究引入了一种使用多式联运图像进行海事船舶识别的新方法. 将可见和红外数据与深度学习相结合,在具有挑战性的条件下显著提高了准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 海事监督部门的监督工作
背景情况:
- 由于环境因素影响图像质量,海上船舶的识别具有挑战性.
- 使用单一源可见图像的现有方法往往具有较低的准确性.
- 多模式成像为更强大的船舶检测提供了潜在的潜力.
研究的目的:
- 开发使用多式联运图像准确的海上船舶识别方法.
- 在不利条件下提高船舶识别性能.
- 利用深度学习从各种图像源中提取特征.
主要方法:
- 使用双重卷积神经网络 (CNN) 来从可见和红外图像中提取特征.
- 使用Softmax函数获得分类的概率值.
- 线性加权决策融合最终认可的组合概率.
主要成果:
- 拟议的方法在可见和红外频谱数据集上实现了0.936的识别精度.
- 在RGB-NIR数据集上达到0.818的精度.
- 多式联网方法的性能优于单源传感器方法.
结论:
- 通过双重CNN和决策融合,可见和红外数据的融合提高了海上船舶的识别能力.
- 这种方法为提高船舶检测系统的准确性和可靠性提供了一个有希望的解决方案.
- 与现有的识别技术相比,这种方法显示出更高的性能.
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