This work is supported by 2024 Henan Provincial Higher Education Institutions Key Scientific Research Project Funding Program Establishment (Project Number:24A520053). This study is also supported by Specialized Creation and Integration Characteristic Demonstration Course Construction in Henan Province.

1. Experimental setup and procedure
<ol>
	<li>Load the pre-trained VGG16 model. 
	NOTE: The first step is to load the pre-trained VGG16 model from the Keras library6.
	<ol>
		<li>To load a pre-trained VGG16 model in Python using popular deep learning libraries like PyTorch (see Table of Materials), follow these general steps:
		<ol>
			<li>Import torch. Import torchvision.models as models.</li>
			<li>Load the pre-trained V...

Enhanced Precision in Salient Object Detection Using Deep Neural Networks

<ol>
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The article introduces an end-to-end deep neural network specifically designed for the detection of salient objects in complex environments. The network is composed of two interconnected components: a pixel-level multiscale fully convolutional network (DCL) and a deep encoder-decoder network (DEDN). These components work synergistically, incorporating contextual semantics to generate visual contrasts within multiscale feature maps. Additionally, they leverage both deep and shallow image features to improve the precision ...

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Matlab</td>
			<td>MathWorks</td>
			<td>Matlab R2016a</td>
			<td>MATLAB&#39;s programming interface provides development tools for improving code quality maintainability and maximizing performance. 
			It provides tools for building applications using custom graphical interfaces. 
			It provides tools for combining MATLAB-based algorithms with external applications and languages</td>
		</tr>
		<tr>
			<td>Processor&#160;</td>
			<td>Intel</td>
			<td>11th Gen Intel(R) Core (TM) i5-1135G7 @ 2.40GHz</td>
			<td>64-bit Win11 processor&#160;</td>
		</tr>
		<tr>
			<td>Pycharm</td>
			<td>JetBrains</td>
			<td>PyCharm 3.0</td>
			<td>PyCharm is a Python IDE (Integrated Development Environment) 
			a list of required python: 
			modulesmatplotlib 
			skimage 
			torch 
			os 
			time 
			pydensecrf 
			opencv 
			glob 
			PIL 
			torchvision 
			numpy 
			tkinter</td>
		</tr>
		<tr>
			<td>PyTorch&#160;</td>
			<td>Facebook</td>
			<td>PyTorch 1.4&#160;</td>
			<td>PyTorch is an open source Python machine learning library , based on Torch , used for natural language processing and other applications.PyTorch can be viewed both as the addition of GPU support numpy , but also can be viewed as a powerful deep neural network with automatic derivatives .</td>
		</tr>
	</tbody>
</table>

end-to-end deep neural network for salient object detection in complex environments

Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit diminished precision when tasked with detecting salient objects within intricate and multifaceted environments. In light of this pressing concern, this article presents an end-to-end deep neural network that aims to detect salient objects within complex environments. The study introduces an end-to-end deep neural network that aims to detect salient objects within complex environments. Comprising two interrelated components, namely a pixel-level multiscale full convolutional network and a deep encoder-decoder network, the proposed network integrates contextual semantics to produce visual contrast across multiscale feature maps while employing deep and shallow image features to improve the accuracy of object boundary identification. The integration of a fully connected conditional random field (CRF) model further enhances the spatial coherence and contour delineation of salient maps. The proposed algorithm is extensively evaluated against 10 contemporary algorithms on the SOD and ECSSD databases. The evaluation results demonstrate that the proposed algorithm outperforms other approaches in terms of precision and accuracy, thereby establishing its efficacy in salient object detection within complex environments.

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

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Our research range from salient object detection, computer vision, image processing and more. We want to respond to the application of salient object detection such as its use in smart grid for inspecting insulators. The next research advancement involves transferring salient object algorithms for learning in the context of smart grids.The convolutional neural networks can more effectively act as salient information from images, enhancing the processing of salient object detection. Therefore, they advance the research in salient object detection. Our research outcome improves the accuracy of salient object detection, and allow for the transfer of algorithms such as their application in smart grids for effectively segmenting and detecting insulators.

This study introduces an end-to-end deep neural network comprising two complementary networks: a pixel-level multi-scale fully convolutional network and a deep encoder-decoder network. The first network integrates contextual semantics to derive visual contrasts from multi-scale feature maps, addressing the challenge of fixed receptive fields in deep neural networks across different layers. The second network utilizes both deep and shallow image features to mitigate the issue of blurred boundaries in target objects. Final...

Watch this Scientific Journal Video about End-To-End Deep Neural Network for Salient Object Detection in Complex Environments at JoVE.com

The present protocol describes a novel end-to-end salient object detection algorithm. It leverages deep neural networks to enhance the precision of salient object detection within intricate environmental contexts.

Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit ...

end-to-end-deep-neural-network-for-salient-object-detection-complex

Research

JoVE Journal

Engineering

231 ビュー.  Zhengzhou University of Economics and Business. 私たちの研究は、顕著な物体検出、コンピュータービジョン、画像処理など多岐にわたります。絶縁体検査のスマートグリッドへの応用など、突出物検出の応用にも対応したい。次の研究の進歩は、スマートグリッドのコンテキストで学習するための顕著なオブジェクトアルゴリズムの転送です。畳み込みニューラルネットワークは、画像からの顕著な情報として?...

ビデオ: 著者スポットライト:スマートグリッドアプリケーションのための顕著な物体検出の強化

The present protocol describes a novel end-to-end salient object detection algorithm. It leverages deep neural networks to enhance the precision of salient object detection within intricate environmental...

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cryo-electron microscopy screening automation across multiple grids using smart leginon

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Cryo-electron microscopy (cryoEM) multi-grid screening is often a tedious process that demands hours of attention. This protocol shows how to set up a standard Leginon collection and Smart Leginon Autoscreen to automate this process. This protocol can be applied to the majority of cryoEM holey foil...

Accelerating Macromolecular Structural Analysis Using Smart Leginon Autoscreen

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using smart leginon autoscreen for cryoem grid screening

Using Smart Leginon Autoscreen for CryoEM Grid Screening

implementation of the dcl algorithm using pre-trained vgg16 model for salient object detection in complex environments

Implementation of the DCL Algorithm Using Pre-Trained VGG16 Model for Salient Object Detection in Complex Environments

mesenchymal stromal cell culture and delivery in autologous conditions: a smart approach for orthopedic applications

Mesenchymal Stromal Cell Culture and Delivery in Autologous Conditions: A Smart Approach for Orthopedic Applications

Culturing human Mesenchymal Stromal Cells (hMSCs) with autologous serum, reduces the risks of rejection by xenogeneic material and other negative effects. It also allows for the recovery of a subset of mesodermal progenitors, which can deliver fresh hMSCs. Embedding hMSCs in an autologous fibrin clot enables easy handling and effective surgical...

hybrid printing for the fabrication of smart sensors

Hybrid Printing for the Fabrication of Smart Sensors

Here we present a protocol for the fabrication of inkjet-printed multilayer sensor structures on additively manufactured substrates and...

transcranial direct current stimulation (tdcs) for memory enhancement

Transcranial Direct Current Stimulation tDCS for Memory Enhancement

A protocol for memory enhancement by using transcranial direct current stimulation (tDCS) targeting dorsolateral prefrontal and posterior parietal cortices, as core cortical nodes within hippocampo-cortical network, is presented. The protocol has been well evaluated in healthy-participant studies and is applicable to aging and dementia research as well.

Transcranial Direct Current Stimulation (tDCS) for Memory Enhancement

rapid quantification of oxidized and reduced forms of glutathione using ortho -phthalaldehyde in cultured mammalian cells in vitro

Author Spotlight: Innovating Thiol Quantification and Biomarker Detection for Oxidative Stress Research 

Quantification of both oxidized and reduced forms of glutathione (GSSG and GSH, respectively) has been achieved through the use of Ortho-phthalaldehyde (OPA). OPA becomes highly fluorescent once conjugated to GSH but is unable to conjugate GSSG until reduced. Here, we describe a multiparametric assay to quantify both using protein quantification for...

GSH/GSSG Measurement &amp;#8211; A Refined OPA Assay for Antioxidant Research

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portable paper-based immunoassay combined with smartphone application for colorimetric and quantitative detection of dengue ns1 antigen

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Addressing urgent dengue diagnostic needs, here we introduce a smartphone app-integrated Dengue NS1 Paper-based Analytical Device (DEN-NS1-PAD) for quantifying Dengue NS1 antigen concentration in clinical serum/blood samples. This innovation enhances dengue management by aiding clinical decision-making in various healthcare settings, even resource-limited ones.

Smartphone-Integrated Dengue NS1 Paper-Based Diagnostic Device

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sample preparation for in situ cryotomography of mammalian cells

Author Spotlight: Optimizing Grid Preparation for Enhanced Cryoelectron Tomography 

This method provides an accessible and flexible protocol for the preparation of electron microscopy (EM) grids for in situ cellular cryotomography and correlative light and electron microscopy...

Preparation of Electron Microscopy Grids for In Situ Cellular Cryotomography

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fast grid preparation for time-resolved cryo-electron microscopy

Fast Grid Preparation for Time-Resolved Cryo-Electron Microscopy

Here, we provide a detailed protocol for the use of a rapid grid making device for both fast grid-making and for rapid mixing and freezing to conduct time-resolved...

novel object recognition and object location behavioral testing in mice on a budget

Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

Here we provide a protocol which includes comprehensive instructions for the economical establishment of murine object location and novel object recognition behavioral testing, including the design, cost, and construction of required equipment as well as execution of behavioral testing, data collection, and...

streptavidin-affinity grid fabrication for cryo-electron microscopy sample preparation

Author Spotlight: Enhancing Cryo-EM Sample Preparation with Streptavidin-Biotin Approach

A step-by-step protocol for fabricating streptavidin affinity grids is provided for use in structural studies of challenging macromolecular samples by cryo-electron...

Streptavidin-Affinity Grid Fabrication for Cryo-Electron Microscopy Sample Preparation

application of monolayer graphene to cryo-electron microscopy grids for high-resolution structure determination

Author Spotlight: Enhancing CryoEM Sample Preparation Using Graphene Monolayer on Microscopy Grids 

The application of support layers to cryogenic electron microscopy (cryoEM) grids can increase particle density, limit interactions with the air-water interface, reduce beam-induced motion, and improve the distribution of particle orientations. This paper describes a robust protocol for coating cryoEM grids with a monolayer of graphene for improved cryo-sample...

Coating CryoEM Grids with Monolayer Graphene to Improve Cryo-Sample Preparation

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alternative methods for the detection of superoxide anion generation in platelets 

Author Spotlight: Innovative Techniques for ROS Detection and Implications for Platelet Research

The generation of superoxide anion is essential for the stimulation of platelets and, if dysregulated, critical for thrombotic diseases. Here, we present three protocols for the selective detection of superoxide anions and the study of redox-dependent platelet...

Detection of Superoxide Anion in Platelets Using Fluorescence Imaging and EPR

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novel object recognition test for the investigation of learning and memory in mice

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

The object recognition test (ORT) is a simple and efficient assay for evaluating learning and memory in mice. The methodology is described...

quantitative polymerase chain reaction (qpcr)-based rapid diagnosis of helicobacter pylori infection and antibiotic resistance

Author Spotlight: Detection and Treatment of Helicobacter pylori Infection 

The protocol presents a noninvasive method for the rapid diagnosis of Helicobacter pylori stomach infections through the string test and determines its antibiotic resistance to clarithromycin and levofloxacin using quantitative polymerase chain reaction...

qPCR-Based Rapid, Noninvasive Diagnosis of &lt;em&gt;Helicobacter pylori&lt;/em&gt; Infection and Antibiotic Resistance

crispr-cas-mediated multianalyte synthetic urine biomarker test for portable diagnostics

Author Spotlight: Engineering Molecular Tools for Disease Detection and Imaging

This protocol describes a CRISPR-Cas-mediated, multianalyte synthetic urine biomarker test that enables point-of-care cancer diagnostics through the ex vivo analysis of tumor-associated protease...

CRISPR-Cas Synthetic Urine Biomarker Test for Cancer Diagnostics

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stimulated single fiber electromyography (sfemg) for assessing neuromuscular junction transmission in rodent models

Author Spotlight: Translational Applications of Stimulated SFEMG in Rodent Models

In this study, we demonstrate a refined single fiber electromyography (SFEMG) protocol to allow in vivo measurement of neuromuscular junction (NMJ) transmission in rodent models. A step-by-step approach to the SFEMG technique is described to allow quantification of NMJ transmission variability and failure in rat gastrocnemius muscle.

SFEMG in Rodents: Assessing NMJ Function in Health and Disease

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To begin, load a deep learning library in Python, such as PyTorch. Import Torch and Torchvision Models as models. Next, load the pre-trained VCG16 model.To generate the pseudo code of the DCL algorithm, provide image dataset SOD into the input field and used train DCL model as the output field. Now initialize the DCL model with the VGG16 backbone network. Pre-process the image dataset, then split the dataset into training and validation sets.Define the loss function for training the DCL model. Set the training hyperparameters as 0.0001 for learning rate, 50 as the number of training epochs set, eight as the batch size, and Adam as the optimizer. Combine the outputs of the DCL and DEDN networks and refine the saliency map using a fully connected conditional random field model.To process the image, click on the run code to bring up the GUI interface. Now press Open Image to choose the selected image for detection. Then press Display Image to show the selected image.Click on Start Detection to detect the selected image. Lastly, press Select the Safe Path and choose the appropriate file location to save the image results. The removal of the DCL model from the algorithm caused a decrease in F beta value and an increase in the E MAE value.This algorithm only removes the DEDN structure. A similar decrease in the F beta value and an increase in the E MAE value were observed compared to the complete module. The DCL algorithm described the target boundary when detecting images in the SOD database, but struggled to effectively filter the background.However, the DEDN algorithm strengthened the target boundary, but suppressed background redundancy information.