Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

154
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
154
Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K
Deconvolution01:20

Deconvolution

295
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
295
Detection of Black Holes01:10

Detection of Black Holes

2.3K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.3K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.4K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

199
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
199

You might also read

Related Articles

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

Sort by
Same author

Tracking the Night: Measuring Age and Sex Patterns in Sleep Duration Using Wearable Technology.

Sleep·2026
Same author

AI‑Enhanced Smartwatch AHI Estimation and AI‑Scored Polysomnography for Obstructive Sleep Apnea: Real‑World Validation.

Nature and science of sleep·2025
Same author

Validating a Consumer Smartwatch for Nocturnal Respiratory Rate Measurements in Sleep Monitoring.

Sensors (Basel, Switzerland)·2023
Same author

Performance evaluation of a wrist-worn reflectance pulse oximeter during sleep.

Sleep health·2022
Same author

Optical recording of neural responses to gold-nanorod mediated photothermal neural inhibition.

Journal of neuroscience methods·2022
Same author

Application of a FL Time Series Building Model in Mobile Network Interaction Anomaly Detection in the Internet of Things Environment.

Computational intelligence and neuroscience·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

682

Coastal Waste Detection Based on Deep Convolutional Neural Networks.

Chengjuan Ren1, Hyunjun Jung1, Sukhoon Lee1

  • 1Software Convergence Engineering Department, Kunsan National University, Gunsan 54150, Korea.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent system for coastal waste recognition and classification, improving sorting efficiency. The novel deep convolutional neural network achieves 83% mAP, outperforming existing methods.

Keywords:
Faster R-CNNcoastal wastedeep convolutional neural networkenvironmental threat

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.7K

Related Experiment Videos

Last Updated: Oct 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

682
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.7K

Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Coastal waste poses significant threats to marine ecosystems, human life, and economies.
  • Manual waste sorting is inefficient and labor-intensive, necessitating automated solutions.

Purpose of the Study:

  • To develop an intelligent waste recognition and classification system for coastal environments.
  • To enhance the efficiency and accuracy of coastal waste sorting using deep learning.

Main Methods:

  • Developed a novel deep convolutional neural network based on the Faster R-CNN framework.
  • Incorporated multi-scale fusion for small object detection and RoI Align to improve positioning accuracy.
  • Utilized parameter correction and data augmentation to optimize model performance.
  • Created and released the IST-Waste dataset for public research.

Main Results:

  • The developed algorithm achieved a mean Average Precision (mAP) of 83%.
  • Demonstrated significantly improved detection performance compared to standard Faster R-CNN and SSD models.
  • The system exhibited higher accuracy and superior performance over state-of-the-art alternatives.

Conclusions:

  • The novel deep convolutional neural network offers an effective solution for intelligent coastal waste recognition and classification.
  • The developed system significantly enhances sorting efficiency and accuracy, addressing limitations of manual methods.
  • The public IST-Waste dataset will foster further advancements in marine debris management research.