Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Construction of self-assembled rhein-methotrexate nanoparticles and study of their synergistic anti-breast cancer mechanism.

Journal of materials chemistry. B·2026

Same author

Dynamic properties of an SARS-CoV-2 epidemic model via stochastic PINNs.

Infectious Disease Modelling·2026

Same author

Defect-Functionalization-Mediated Tunneling Drives Nonlinear Photoemission in Perovskites.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

The impact of the digital divide on residents' healthcare consumption inequality: evidence from CFPS in China.

Frontiers in public health·2026

Same author

Implementing martial arts education in Chinese schools: teachers' perspectives on the school martial arts program.

Frontiers in sports and active living·2025

Same author

Design of prodrugs with reactive oxygen species as activators and their application in tumor therapy.

Theranostics·2025

Search research articles

Related Experiment Video

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

Research on floating object classification algorithm based on convolutional neural network.

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

Summary

This summary is machine-generated.

This study uses artificial intelligence and a modified VGG-16 model for water surface garbage classification, achieving 93.86% accuracy. The enhanced model improves unmanned boat environmental protection capabilities.

Keywords:

Convolutional Neural Network Data Augmentation Image Recognition Surface Floating Debris Unmanned Boat

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last 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

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Artificial Intelligence
Environmental Science
Computer Vision

Background:

Unmanned boats and AI show promise for water surface garbage classification.
Deep learning models, specifically Convolutional Neural Networks (CNNs), are effective for feature extraction of floating objects.

Purpose of the Study:

To develop and optimize a VGG16-15 model for classifying 15 types of water surface floatables using AI.
To enhance the generalization capability of the model through customized improvements and data augmentation.

Main Methods:

A dataset of 5707 images across 15 categories was curated for training and validation.
The VGG-16 architecture was customized, incorporating learning rate decay, early stopping, and data augmentation.
Model performance was analyzed by varying epochs and batch sizes, with optimal results at 20 epochs and batch size 64.

Main Results:

The VGG16-15 model achieved a recognition accuracy of 93.86%, a significant improvement over the base VGG-16 model.
Data augmentation increased accuracy by 4.91%, highlighting its importance for model generalization.
Few-shot testing confirmed the fine-tuned model's enhanced generalization capabilities.

Conclusions:

The customized VGG16-15 model effectively classifies water surface garbage, demonstrating the power of transfer learning.
This research provides technical support for deploying unmanned boats in environmental protection efforts.
AI-driven classification systems are crucial for improving water body cleanliness and management.