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Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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相关实验视频

Updated: May 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

研究基于卷积神经网络的浮动物体分类算法.

Jikai Yang¹, Zihan Li¹, Ziyan Gu¹

¹School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.

Scientific reports

|December 31, 2024

概括

此摘要是机器生成的。

这项研究使用人工智能和修改后的VGG-16模型进行水面垃圾分类,达到93.86%的准确性. 改进后的模型提高了无人驾驶船的环境保护能力.

关键词:

卷积神经网络是一个卷积神经网络.数据增强的数据增强.图像识别图像识别表面上漂浮的碎片无人驾驶船只无人驾驶船只

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科学领域:

人工智能的人工智能
环境科学环境科学
计算机视觉计算机视觉

背景情况:

无人驾驶船只和人工智能显示出水面垃圾分类的前景.
深度学习模型,特别是卷积神经网络 (CNN),对于浮动对象的特征提取是有效的.

研究的目的:

开发和优化VGG16-15模型,使用人工智能对15种水面浮物进行分类.
通过定制改进和数据增强,增强模型的概括能力.

主要方法:

一个包含15个类别的5707张图像的数据集被策划用于培训和验证.
针对VGG-16的架构进行了定制,包括学习速度衰减,早期停止和数据增强.
模型性能根据不同的时代和批量大小进行分析,在20个时代和批量大小64时获得最佳结果.

主要成果:

VGG16-15模型实现了93.86%的识别精度,比VGG-16基本模型有了显著的改进.
数据增强增加了4.91%的准确性,突出了其对模型概括的重要性.
短时间的测试证实了微调模型的增强泛化能力.

结论:

定制的VGG16-15模型有效地对水面垃圾进行分类,证明了转移学习的力量.
这项研究为在环境保护工作中部署无人船提供了技术支持.
人工智能驱动的分类系统对于改善水体清洁和管理至关重要.