Name: end-to-end deep neural network for salient object detection in complex environments
Uploaded: 2023-12-15T13:10:00.000+00:00
Duration: 3 min 31 s
Description: Watch this Scientific Journal Video about End-To-End Deep Neural Network for Salient Object Detection in Complex Environments at JoVE.com

本研究由2024年河南省高等学校重点科研项目资助项目建立（项目编号:24A520053）资助。本研究还得到了河南省特色创意与融合特色示范课程建设的支持。

1. 实验设置和程序
<ol><li>加载预训练的 VGG16 模型。 注意:第一步是从 Keras库 6 加载预训练的 VGG16 模型。<ol><li>要使用 PyTorch 等流行的深度学习库在 Python 中加载预训练的 VGG16 模型（请参阅 材料表），请遵循以下常规步骤:<ol><li>导入 火炬。导入 torchvision.models 作为模型。</li>
			<li>加载预训练的 VGG16 模型。vgg16_model = models.vgg16（预训练 = True）。</li>
			<li>确保 VGG16 模型的摘要为"print（vgg16_model）"。</li>
		</ol></li>
	</ol></li>
	<li>定义 DCL 和 DEDN 模型。<ol><li>对于 DCL 算法的伪代码，请提供 输入:图像数据集 SOD 和 输出:训练的 DCL 模型。<ol><li>使用 VGG16 骨干网初始化 DCL 模型 。</li>
			<li>对图像数据集 D 进行预处理（例如，调整大小、归一化）。</li>
			<li>将数据集拆分为训练集和验证集。</li>
			<li>定义用于训练 DCL 模型的损失函数（例如，二元交叉熵）。</li>
			<li>设置训练的超参数:学习率 （0.0001）、训练周期数 （50）、批处理大小为 （8）、优化器 （Adam）。</li>
			<li>训练 DCL 模型:对于定义的 epoch 数中的每个 epoch，对训练集中的每个批处理执行 do。输入以下内容:<ol><li>前向传递:将批处理图像馈送到 DCL 模型。使用预测的显著性图和地面实况图计算损失。</li>
				<li>向后传递:使用梯度下降结束更新模型参数。计算验证集端的验证损失和其他评估指标。</li>
			</ol></li>
			<li>保存经过训练的 DCL 模型。</li>
			<li>返回经过训练的 DCL 模型。</li>
		</ol></li>
		<li>对于 DEDN 算法的伪代码，请输入:图像数据集 （X）、真值显著性图 （Y）、训练迭代次数 （N）。<ol><li>对于编码器网络，请确保编码器基于经过修改的 VGG16 骨架（如下所述）。 注意:encoder_input = 输入（shape=input_shape） encoder_conv1 = Conv2D（64， （3， 3）， activation='relu'， padding='same'）（encoder_input） encoder_pool1 = MaxPooling2D（（2， 2））（encoder_conv1） encoder_conv2 = Conv2D（128， （3， 3）， activation='relu'， padding='same'）（encoder_pool1） encoder_pool2 = MaxPooling2D（（2， 2））（encoder_conv2） encoder_conv3 = Conv2D（256， （3， 3）， activation='relu'， padding='same'）（encoder_pool2） encoder_pool3 = MaxPooling2D（（2， 2））（encoder_conv3）</li>
			<li>对于解码器网络，请确保解码器基于经过修改的 VGG16 骨架（如下所述）。 注意:decoder_conv1 = Conv2D（256， （3， 3）， activation='relu'， padding='same'）（encoder_pool3） decoder_upsample1 = UpSampling2D（（2， 2））（decoder_conv1） decoder_conv2 = Conv2D（128， （3， 3）， activation='relu'， padding='same'）（decoder_upsample1） decoder_upsample2 = UpSampling2D（（2， 2））（decoder_conv2） decoder_conv3 = Conv2D（64， （3， 3）， activation='relu'， padding='same'）（decoder_upsample2） decoder_upsample3 = UpSampling2D（（2， 2））（decoder_conv3） decoder_output = Conv2D（1， （1， 1）， activation='sigmoid'， padding='same'）（decoder_upsample3）</li>
		</ol></li>
		<li>定义 DEDN 模型。模型 = 模型（输入 = encoder_input，输出 = decoder_output）。</li>
		<li>编译模型。model.compile （优化器 = Adam，损失 = binary_crossentropy）。</li>
		<li>选择 训练循环。 说明: 对于范围（N）的迭代: # 随机选择一批图像和地面实况图;batch_X，batch_Y = randomly_select_batch（X， Y， batch_size）。<ol><li>在批处理上训练模型。损失 = model.train_on_batch（batch_X， batch_Y）。打印丢失以进行监控。</li>
		</ol></li>
		<li>保存经过训练的模型。model.save （'dedn_model.h5'）。</li>
	</ol></li>
	<li>合。<ol><li>结合 DCL 和 DEDN 网络的输出，并使用全连接条件随机场 （CRF） 模型优化显著性图。</li>
	</ol></li>
</ol>2. 图像处理
<ol><li>单击 run code 以调出 GUI 界面（图 4）。</li>
	<li>单击 打开图像 以选择路径，从而选择要检测的图像。</li>
	<li>单击 显示图像 以显示已选择用于检测的图像。</li>
	<li>单击 "开始检测 "以检测所选图像。 注意:检测结果将与检测到的图像一起出现，即突出物体的结果（图 5）。</li>
	<li>单击 选择保存路径 以保存显着对象检测的图像结果。</li>
</ol>

使用深度神经网络提高显著目标检测的精度

<ol>
	<li>Wang, W. G., Shen, J. B., Ling, H. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+deep+network+solution+for+attention+and+aesthetics+aware+photo+cropping.">A deep network solution for attention and aesthetics aware photo cropping.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 41 (7), 1531-1544 (2018).</li><li>Wang, W. G., Sun, G. L., Gool, L. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Looking+beyond+single+images+for+weakly+supervised+semantic+segmentation+learning.">Looking beyond single images for weakly supervised semantic segmentation learning.</a> IEEE Transactions on Pattern Analysis and Machine. , (2022).</li><li>Mei, H. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+dense+context+for+salient+object+detection.">Exploring dense context for salient object detection.</a> IEEE Transactions on Circuits and Systems for Video Technology. 32 (3), 1378-1389 (2021).</li><li>Borji, A., Itti, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=State-of-the-art+in+visual+attention+modeling.">State-of-the-art in visual attention modeling.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (1), 185-207 (2012).</li><li>Long, J., Shelhamer, E., Darrell, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fully+convolutional+networks+for+semantic+segmentation.">Fully convolutional networks for semantic segmentation.</a> , 3431-3440 (2015).</li><li>Simonyan, K., Zisserman, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Very+deep+convolutional+networks+for+large-scale+image+recognition.">Very deep convolutional networks for large-scale image recognition.</a> arXiv preprint. , 1409-1556 (2014).</li><li>He, K., Zhang, X., Ren, S., Sun, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+residual+learning+for+image+recognition.">Deep residual learning for image recognition.</a> , 770-778 (2016).</li><li>Liu, N., Han, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dhsnet:+Deep+hierarchical+saliency+network+for+salient+object+detection.">Dhsnet: Deep hierarchical saliency network for salient object detection.</a> , 678-686 (2016).</li><li>Zhang, L., Dai, J., Lu, H., He, Y., Wang, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+bi-directional+message+passing+model+for+salient+object+detection.">A bi-directional message passing model for salient object detection.</a> , 1741-1750 (2018).</li><li>Wu, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+mutual+learning+method+for+salient+object+detection+with+intertwined+multi-supervision.">A mutual learning method for salient object detection with intertwined multi-supervision.</a> , 8150-8159 (2019).</li><li>Li, G., Yu, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+contrast+learning+for+salient+object+detection.">Deep contrast learning for salient object detection.</a> , 478-487 (2019).</li><li>Ren, Q., Hu, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-scale+deep+encoder-decoder+network+for+salient+object+detection.">Multi-scale deep encoder-decoder network for salient object detection.</a> Neurocomputing. 316, 95-104 (2018).</li><li>Wang, W. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Salient+object+detection+in+the+deep+learning+era:+An+in-depth+survey.">Salient object detection in the deep learning era: An in-depth survey.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 44 (6), 3239-3259 (2021).</li><li>Zhou, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RGB-D+salient+object+detection:+A+survey.">RGB-D salient object detection: A survey.</a> Computational Visual Media. 7, 37-69 (2021).</li><li>Fu, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Siamese+network+for+RGB-D+salient+object+detection+and+beyond.">Siamese network for RGB-D salient object detection and beyond.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 44 (9), 5541-5559 (2021).</li><li>Lai, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Weakly+supervised+visual+saliency+prediction.">Weakly supervised visual saliency prediction.</a> IEEE Transactions on Image Processing. 31, 3111-3124 (2022).</li><li>Zhang, L., Dai, J., Lu, H., He, Y., Wang, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+bi-directional+message+passing+model+for+salient+object+detection.">A bi-directional message passing model for salient object detection.</a> , 1741-1750 (2018).</li><li>Wu, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+mutual+learning+method+for+salient+object+detection+with+intertwined+multi-supervision.">A mutual learning method for salient object detection with intertwined multi-supervision.</a> , 8150-8159 (2019).</li><li>Wang, W., Shen, J., Dong, X., Borji, A., Yang, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inferring+salient+objects+from+human+fixations.">Inferring salient objects from human fixations.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 42 (8), 1913-1927 (2019).</li><li>Movahedi, V., Elder, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+and+perceptual+validation+of+performance+measures+for+salient+object+segmentation.">Design and perceptual validation of performance measures for salient object segmentation.</a> , 49-56 (2010).</li><li>Shi, J., Yan, Q., Xu, L., Jia, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hierarchical+image+saliency+detection+on+extended+CSSD.">Hierarchical image saliency detection on extended CSSD.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 38 (4), 717-729 (2015).</li><li>Achanta, R., Hemami, S., Estrada, F., Susstrunk, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frequency-tuned+salient+region+detection.">Frequency-tuned salient region detection.</a> , 1597-1604 (2009).</li><li>Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Saliency+detection+via+graph-based+manifold+ranking.">Saliency detection via graph-based manifold ranking.</a> , 3166-3173 (2013).</li><li>Wei, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Geodesic+saliency+using+background+priors.">Geodesic saliency using background priors.</a> Computer Vision-ECCV 2012. , 29-42 (2012).</li><li>Margolin, R., Tal, A., Zelnik-Manor, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+makes+a+patch+distinct.">What makes a patch distinct.</a> , 1139-1146 (2013).</li><li>Perazzi, F., Kr&#228;henb&#252;hl, P., Pritch, Y., Hornung, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Saliency+filters:+Contrast+based+filtering+for+salient+region+detection.">Saliency filters: Contrast based filtering for salient region detection.</a> , 733-740 (2012).</li><li>Hou, X., Harel, J., Koch, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+signature:+Highlighting+sparse+salient+regions.">Image signature: Highlighting sparse salient regions.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 34 (1), 194-201 (2011).</li><li>Jiang, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Salient+object+detection:+A+discriminative+regional+feature+integration+approach.">Salient object detection: A discriminative regional feature integration approach.</a> , 2083-2090 (2013).</li><li>Li, G., Yu, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual+saliency+based+on+multiscale+deep+features.">Visual saliency based on multiscale deep features.</a> , 5455-5463 (2015).</li><li>Lee, G., Tai, Y. W., Kim, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+saliency+with+encoded+low+level+distance+map+and+high-level+features.">Deep saliency with encoded low level distance map and high-level features.</a> , 660-668 (2016).</li><li>Liu, N., Han, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dhsnet:+Deep+hierarchical+saliency+network+for+salient+object+detection.">Dhsnet: Deep hierarchical saliency network for salient object detection.</a> , 678-686 (2016).</li></ol>

作者没有什么可透露的。

本文介绍了一种端到端深度神经网络，专门设计用于检测复杂环境中的突出物体。该网络由两个相互连接的组件组成:像素级多尺度全卷积网络 （DCL） 和深度编码器-解码器网络 （DEDN）。这些组件协同工作，结合上下文语义，在多尺度特征图中生成视觉对比。此外，它们还利用深和浅图像特征来提高物体边界描绘的精度。全连接条件随机场（CRF）模型的集成进一步增强了显著性图和等高线描绘的?...

突出物体检测模仿人类的视觉注意力，快速识别关键图像区域，同时抑制背景信息。该技术被广泛用作图像裁剪1、语义分割2 和图像编辑3 等任务中的预处理工具。它简化了背景替换和前景提取等任务，提高了编辑效率和精度。此外，它还通过增强目标定位来帮助语义分割。显著目标检测在提高计算效率和节省内存方面的潜力凸显了其重要的研究和应用前景。
多年来，显著目标检测已经从最初的传统算法发展到深度学习算法的结合。这些进步的目的是缩小显著物体检测与人类视觉机制之间的差距。这导致了深度卷积网络模型的采用，用于显著目标检测的研究。Borji等人[4 ]总结并推广了大多数依赖于图像底层特征的经典传统算法。尽管检测精度有所提高，但手动经验和认知能力仍然给复杂环境中的显著物体检测带来挑战。
卷积神经网络 （CNN） 的使用在显著目标检测领域很普遍。在这种情况下，深度卷积神经网络被用于通过自主学习进行权重更新。卷积神经网络通过级联卷积层和池化层从图像中提取上下文语义，能够在更高层次上学习复杂的图像特征，这些特征在不同环境下的显著目标检测中具有更高的辨别和表征能力。
2016 年，全卷积神经网络5 作为一种流行的显著性目标检测方法获得了巨大的关注，研究人员在此基础上开始了像素级显著性目标检测。许多模型通常建立在现有网络上（例如，VGG166、ResNet7），旨在增强图像表示并增强边缘检测效果。
Liu等人[8 ]使用已经训练过的神经网络作为框架来全局计算图像，然后使用分层网络细化对象边界。两个网络的组合形成了最终的深度显著性网络。这是通过以重复的方式将先前获得的显著地图作为先验知识输入网络来实现的。Zhang等[9 ]利用深度网络有效地融合了图像语义和空间信息，分别实现了从浅层到深层和从深层到浅层的双向信息传递。Wu等人10提出了使用相互学习深度模型检测显著对象的方法。该模型利用卷积神经网络中的前景和边缘信息来促进检测过程。Li等[11 ]利用神经网络的"空穴算法"来解决在显著目标检测的背景下固定深度神经网络中不同层的感受野的挑战。然而，超像素分割用于对象边缘采集，大大增加了计算工作量和计算时间。任等人[12 ]设计了一个多尺度的编码器-解码器网络来检测显著的物体，并利用卷积神经网络有效地结合了深层和浅层特征。尽管通过这种方法解决了目标检测中边界模糊的挑战，但信息的多尺度融合不可避免地导致了更高的计算需求。
文献综述13 提出，总结了显著性检测从传统方法到深度学习方法，可以非常清楚地看到显著性目标检测从起源到深度学习时代的演变。文献14中提出了各种基于RGB-D的显著目标检测模型，并具有良好的性能。以上文献对各种类型的显著性目标检测算法进行了综述和分类，并介绍了它们的应用场景、使用的数据库和评价指标。本文还对所提出的算法进行了定性和定量分析，涉及其建议的数据库和评估指标。
上述算法在公共数据库中都取得了显著的成果，为复杂环境下的显著目标检测提供了依据。尽管在国内和国际上，这一领域的研究成果众多，但仍有一些问题需要解决。（1）传统的非深度学习算法由于依赖颜色、纹理、频率等人工标注的特征，容易受到主观体验和感知的影响，因此精度往往较低。因此，其显著目标检测能力的精度降低。使用传统的非深度学习算法检测复杂环境中的突出对象具有挑战性，因为它们难以处理复杂的场景。（2）传统的显著目标检测方法由于依赖于手动标记的特征（如颜色、纹理和频率）而表现出有限的准确性。此外，区域级检测的计算成本可能很高，通常会忽略空间一致性，并且往往无法检测对象边界。这些问题需要得到解决，以提高显著目标检测的精度。（3）在复杂环境中进行显著目标检测对大多数算法来说是一个挑战。由于具有可变背景（相似的背景和前景颜色、复杂的背景纹理等）的显著目标检测环境日益复杂，检测目标大小不一致等不确定性以及前景和背景边缘定义不明确等诸多不确定性，大多数显著目标检测算法都面临着严峻的挑战。
在具有相似背景和前景颜色、复杂背景纹理和模糊边缘的复杂环境中，大多数当前算法在检测显着对象方面的准确性较低。尽管目前基于深度学习的显著对象算法表现出比传统检测方法更高的准确性，但它们利用的底层图像特征在有效表征语义特征方面仍然不足，其性能仍有改进的空间。
综上所述，该文提出一种端到端的深度神经网络，旨在提高复杂环境下显著目标检测的准确性，改善目标边缘，更好地表征语义特征。本文的贡献如下:（1）第一个网络采用VGG16作为基础网络，并使用"孔算法"11修改其五个池化层。像素级多尺度全卷积神经网络从不同的空间尺度学习图像特征，解决了深度神经网络各层静态感受野的挑战，提高了场内重要焦点区域的检测精度。（2）最近提高显著目标检测准确性的努力集中在利用更深的神经网络，如VGG16，从编码器网络中提取深度特征，从解码器网络中提取浅层特征。这种方法有效地提高了对象边界的检测精度，并改善了语义信息，特别是在背景多变、对象大小不一致、前景和背景边界模糊的复杂环境中。（3）近年来，为了提高显著目标检测的精度，人们强调使用更深的网络，包括VGG16，从编码器网络中提取深层特征，从解码器网络中提取浅层特征。这种方法已经证明了对对象边界的改进检测和更多的语义信息，特别是在具有不同背景、对象大小以及前景和背景之间边界模糊的复杂环境中。此外，还实现了全连接条件随机场（CRF）模型的集成，以增强显著地图的空间连贯性和等高线精度。在具有复杂背景的 SOD 和 ECSSD 数据集上评估了这种方法的有效性，并发现具有统计学意义。
相关工作 Fu et al.15 提出了一种使用 RGB 和深度学习进行显著目标检测的联合方法。Lai等[16 ]引入了一种弱监督模型，用于显著性对象检测，从注释中学习显著性，主要利用涂鸦标签来节省注释时间。虽然这些算法融合了两个互补的显著性目标检测网络，但它们缺乏对复杂场景下显著性检测的深入研究。Wang等[17 ]设计了一种自下而上和自上而下的神经网络特征的双模迭代融合，逐步优化前一次迭代的结果，直到收敛。Zhang等[18 ]利用深度网络有效地融合了图像语义和空间信息，分别实现了从浅层到深层和从深层到浅层的双向信息传递。Wu等19提出了使用相互学习深度模型检测显著对象的方法。该模型利用卷积神经网络中的前景和边缘信息来促进检测过程。这些基于深度神经网络的显著性目标检测模型在公开可用的数据集上取得了卓越的性能，能够在复杂的自然场景中实现显著目标检测。尽管如此，设计更优越的模型仍然是该研究领域的一个重要目标，也是本研究的主要动机。
总体框架 如 图 1 所示，所提出的模型的示意图主要源自 VGG16 架构，包括像素级多尺度全卷积神经网络 （DCL） 和深度编码器-解码器网络 （DEDN）。该模型消除了 VGG16 的所有最终池化层和全连接层，同时适应了 W × H 的输入图像尺寸。操作机制涉及通过DCL对输入图像进行初始处理，便于提取深层特征，而浅层特征则从DEDN网络获得。随后，这些特征的合并受到全连接条件随机场（CRF）模型的影响，增强了所生成的显著性图的空间连贯性和等高线精度。
为了确定该模型的有效性，它在具有复杂背景的 SOD20 和 ECSSD21 数据集上进行了测试和验证。输入图像通过DCL后，得到具有不同感受野的不同比例特征图，并结合上下文语义，生成具有维度间连贯性的W×H显著图。DCL 采用一对具有 7 x 7 内核的卷积层来替代原始 VGG16 网络的最终池化层，从而增强了特征图中空间信息的保存。这与上下文语义相结合，产生了具有跨维度连贯性的 W × H 显著图。同样，深度编码器-解码器网络 （DEDN） 利用解码器中具有 3 x 3 内核的卷积层，并在最后一个解码模块之后使用单个卷积层。利用图像的深层和浅层特征，可以生成空间维度为 W × H 的突出地图，从而解决物体边界模糊的挑战。该研究描述了一种开创性的显著目标检测技术，该技术将 DCL 和 DEDN 模型合并到一个统一的网络中。通过训练过程学习这两个深度网络的权重，并合并生成的显著性图，然后使用完全连接的条件随机场 （CRF） 进行细化。这种改进的主要目的是提高空间一致性和等高线定位。
像素级多尺度全卷积神经网络 VGG16 架构最初由五个池化层组成，每个池化层的步幅为 2。每个池化层压缩图像大小以增加通道数，从而获得更多上下文信息。DCL 模型的灵感来自文献13 ，是对 VGG16 框架的改进。在本文中，使用了像素级 DCL 模型11 ，如图 2 所示，位于深度卷积神经网络 VGG16 的架构中。最初的四个最大池层与三个内核互连。第一个内核是 3 × 3 × 128;第二个内核是 1 × 1 × 128;第三个内核是 1 × 1 × 1。为了在连接到三个内核的初始四个池化层之后实现统一大小的特征图，每个池化层的大小相当于原始图像的八分之一，连接到这四个最大池化层的第一个内核的步长分别设置为 4、2、1 和 1。
为了在不同的核中保留原始感受野，文献11 中提出的"空穴算法"用于通过添加零来扩展核的大小，从而保持核的完整性。这四个特征图以不同的步长连接到第一个内核。因此，在最后阶段生成的特征图具有相同的尺寸。这四个特征图构成了一组从不同尺度获得的多尺度特征，每个特征代表不同大小的感受野。从四个中间层获得的结果特征图与从 VGG16 派生的最终特征图连接起来，从而生成 5 通道输出。随后的输出随后受到具有 S 形激活函数的 1 × 1 × 1 内核的影响，最终产生显著图（分辨率为原始图像的八分之一）。使用双线性插值对图像进行上采样和放大，确保生成的图像（称为显著性图）保持与初始图像相同的分辨率。
深度编码器-解码器网络 同样，VGG16网络被用作骨干网络。VGG16的特点是浅特征图通道数量少，但分辨率高，深特征通道数量多，但分辨率低。池化层和下采样提高了深度网络的计算速度，但代价是降低了其特征图分辨率。为了解决这个问题，根据文献14中的分析，编码器网络用于修改原始VGG16中最后一个池化层的完全连通性。这种修改包括用两个具有 7 × 7 个内核的卷积层替换它（较大的卷积内核增加了感受野）。两个卷积核都配备了归一化 （BN） 操作和修改后的线性单元 （ReLU）。此调整将生成编码器输出特征图，以更好地保留图像空间信息。
虽然编码器改进了显著对象全局定位的高级图像语义，但其显著对象的边界模糊问题并未得到有效改善。为了解决这个问题，受边缘检测工作12 的启发，将深层特征与浅层特征融合在一起，提出了编码器-解码器网络模型 （DEDN），如 图 3 所示。编码器架构包括三个与初始四个内核互连的内核，而解码器则使用从最大池化层检索到的最大值系统地增强特征图分辨率。
在这种用于显著对象检测的创新方法中，在解码器阶段，将具有 3 × 3 内核的卷积层与批量归一化层和自适应线性单元结合使用。在解码器架构中的最终解码模块结束时，采用孤信道卷积层来获取空间维度 W × H 的显着映射。显著图是通过编码器-解码器模型的协同融合生成的，产生了结果，以及两者的互补融合，即深层信息和浅层信息的互补融合。这不仅实现了对突出物体的精确定位，增加了感受野，而且有效地保留了图像细节信息，加强了突出物体的边界。
集成机制 编码器架构由三个内核组成，它们与 VGG16 模型的初始四个最大池化层相关联。相比之下，解码器被特意制定为通过利用从相应的池化层获得的最大值来逐步增强从上采样层获取的特征图的分辨率。然后在解码器中使用利用 3 x 3 内核的卷积层、批量归一化层和修改后的线性单元，然后是单通道卷积层以生成维度 W × H 的显着映射。两个深度网络的权重是通过交替的训练周期来学习的。第一个网络的参数保持固定，而第二个网络的参数总共经过了五十个周期的训练。在此过程中，用于融合的显著性图（S1 和 S2）的权重通过随机梯度进行更新。损失函数11 为:
<img alt="Equation 1" src="/files/ftp_upload/65554/65554eq01.jpg" style="margin: auto;"> (1)
在给定的表达式中，符号 G 表示手动标记的值，而 W 表示完整的网络参数集。i β权重作为平衡因子，用于调节计算过程中显著像素与非显著像素的比例。
图像 I 的特征有三个参数: |我|， |我|- 和 |我|+，分别表示像素总数、非显著像素数和显著像素数。 <img alt="Equation 2" src="/files/ftp_upload/65554/65554eq02.jpg" style="margin: auto;">
由于从上述两个网络获得的显著图没有考虑相邻像素的相干性，因此使用全连接像素级显著性细化模型CRF15 来提高空间相干性。能量方程11 如下，求解二进制像素标记问题。
<img alt="Equation 3" src="/files/ftp_upload/65554/65554eq03.jpg" style="margin: auto;"> (2)
其中 L 表示分配给所有像素的二进制标签（显著值或非显著值）。变量 P（li） 表示给定像素 xi被分配特定标签 li 的可能性，表示像素 xi显著性的可能性。一开始，P（1） = Si和 P（0） = 1 - Si，其中 Si表示融合显著性图 S 中像素 xi处的显著性值。θi，j（li，l j） 是成对势位，定义如下。
<img alt="Equation 4" src="/files/ftp_upload/65554/65554eq04.jpg" style="margin: auto;"> (3)
其中，如果 li≠ lj，则 μ（li，l j） = 1，否则 μ（li，l j） = 0。θi，j 的计算涉及两个核的使用，其中初始核取决于像素位置 P 和像素强度 I。这导致具有相似颜色的像素具有相似的显著性值。σ α 和σβ这两个参数调节了颜色相似性和空间接近性对结果的影响程度。第二个内核的目标是消除孤立的小区域。能量的最小化是通过高维滤波实现的，它加快了条件随机场 （CRF） 分布的平均场。经过计算，表示为 Scrf 的显著性地图与检测到的显著性物体相比表现出增强的空间连贯性和等高线。
实验性配置 本文利用Python构建了一个基于VGG16神经网络的深度目标检测网络。使用SOD20 和ECSSD21 数据集将所提出的模型与其他方法进行了比较。SOD 图像数据库以其复杂而杂乱的背景、前景和背景之间的颜色相似以及较小的物体尺寸而闻名。此数据集中的每张图像都被分配了一个手动标记的真实值，用于定量和定性性能评估。另一方面，ECSSD数据集主要由来自互联网的图像组成，具有更复杂和逼真的自然场景，图像背景和突出物体之间的对比度较低。
本文用于比较模型的评价指标包括常用的Precision-Recall曲线、 Fβ和 EMAE。为了定量评估预测的显著性图，采用精确召回率 （P-R） 曲线22 将阈值从 0 更改为 255 以对显著性图进行二值化。 Fβ是一个综合评估指标，使用从二值显著性图和真实值图派生的精度和召回率方程计算。
<img alt="Equation 5" src="/files/ftp_upload/65554/65554eq05.jpg" style="margin: auto;"> (4)
其中 β 是用于调整准确率和召回率的权重参数，设置 β2 = 0.3。 EMAE的计算等效于计算结果显著性图和地面实况图之间的平均绝对误差，如下图所定义:
<img alt="Equation 6" src="/files/ftp_upload/65554/65554eq06.jpg" style="margin: auto;"> (5)
设 Ts（u，v） 表示显著映射 （u，v） 像素的提取值，设 TG（u，v） 表示真实映射 （u，v） 像素的相应值。

<table><tbody><tr><td>Matlab</td><td>MathWorks</td><td>Matlab R2016a</td><td>MATLAB&#039;s programming interface provides development tools for improving code quality maintainability and maximizing performance.&lt;br/&gt; It provides tools for building applications using custom graphical interfaces.&lt;br/&gt; It provides tools for combining MATLAB-based algorithms with external applications and languages</td></tr><tr><td>Processor&amp;nbsp;</td><td>Intel</td><td>11th Gen Intel(R) Core (TM) i5-1135G7 @ 2.40GHz</td><td>64-bit Win11 processor&amp;nbsp;</td></tr><tr><td>Pycharm</td><td>JetBrains</td><td>PyCharm 3.0</td><td>PyCharm is a Python IDE (Integrated Development Environment)&lt;br/&gt; a list of required python:&lt;br/&gt; modulesmatplotlib&lt;br/&gt; skimage&lt;br/&gt; torch&lt;br/&gt; os&lt;br/&gt; time&lt;br/&gt; pydensecrf&lt;br/&gt; opencv&lt;br/&gt; glob&lt;br/&gt; PIL&lt;br/&gt; torchvision&lt;br/&gt; numpy&lt;br/&gt; tkinter</td></tr><tr><td>PyTorch&amp;nbsp;</td><td>Facebook</td><td>PyTorch 1.4&amp;nbsp;</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

突出目标检测已成为计算机视觉领域中一个新兴的兴趣领域。然而，当任务是在复杂和多方面的环境中检测突出物体时，流行的算法表现出较低的精度。鉴于这一紧迫问题，本文提出了一种端到端深度神经网络，旨在检测复杂环境中的突出对象。该研究引入了一种端到端的深度神经网络，旨在检测复杂环境中的突出物体。该网络由像素级多尺度全卷积网络和深度编码器-解码器网络两个相互关联的组件组成，该网络集成了上下文语义，在多尺度特征图上产生视觉对比，同时采用深浅图像特征来提高目标边界识别的准确性。全连接条件随机场（CRF）模型的集成进一步增强了显著地图的空间连贯性和等高线描绘。针对SOD和ECSSD数据库上的10种现代算法，对所提出的算法进行了广泛的评估。评估结果表明，所提算法在精度和准确度方面优于其他方法，从而确立了其在复杂环境中显著目标检测的有效性。

本研究引入了一个由两个互补网络组成的端到端深度神经网络:像素级多尺度全卷积网络和深度编码器-解码器网络。第一个网络集成了上下文语义，从多尺度特征图中推导出视觉对比，解决了跨不同层的深度神经网络中固定感受野的挑战。第二个网络利用深和浅图像特征来缓解目标对象边界模糊的问题。最后，应用全连接条件随机场（CRF）模型提高显著性图的空间相干性和等高线。
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Watch this Scientific Journal Video about End-To-End Deep Neural Network for Salient Object Detection in Complex Environments at JoVE.com

作者聚焦:智能电网应用的显著对象检测增强功能

本协议描述了一种新型的端到端显著目标检测算法。它利用深度神经网络来提高复杂环境背景下显著目标检测的精度。

用于复杂环境中显著目标检测的端到端深度神经网络

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

我们的研究范围包括显著物体检测、计算机视觉、图像处理等。我们希望响应显著物体检测的应用，例如它在智能电网中的使用，用于检测绝缘子。下一个研究进展涉及在智能电网的背景下转移用于学习的显著对象算法。卷积神经网络可以更有效地作为图像中的显著信息，增强显著目标检测的处理能力。因此，他们推进了显著目标检测的研究。我们的研究成果提高了显著目标检测的准确性，并允许算法的转移，例如它们在智能电网中的应用，以有效地分割和检测绝缘体。

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

Research

JoVE Journal

Engineering

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

451 次观看.  Zhengzhou University of Economics and Business. 我们的研究范围包括显著物体检测、计算机视觉、图像处理等。我们希望响应显著物体检测的应用，例如它在智能电网中的使用，用于检测绝缘子。下一个研究进展涉及在智能电网的背景下转移用于学习的显著对象算法。卷积神经网络可以更有效地作为图像中的显著信息，增强显著目标检测的处理能力。因此，他们推进了显著目标检测的研究。我们的研究成果提高了显?...

视频: 作者聚焦:智能电网应用的显著对象检测增强功能

Controlling the Size, Shape and Stability of Supramolecular Polymers in Water

For aqueous based supramolecular polymers, the simultaneous control over shape, size and stability is very difficult1. At the same time, the ability to do so is highly important in view of a number of applications in functional soft matter including electronics, biomedical engineering, and sensors. In the past, successful strategies to control the size and shape of supramolecular polymers typically focused on the use of templates2,3, end cappers4 or selective solvent techniques5. 
Here we disclose a strategy based on self-assembling discotic amphiphiles that leads to the control over stack length and shape of ordered, chiral columnar aggregates. By balancing electrostatic repulsive interactions on the hydrophilic rim and attractive non-covalent forces within the hydrophobic core of the polymerizing building block, we manage to create small and discrete spherical objects6,7. Increasing the salt concentration to screen the charges induces a sphere-to-rod transition. Intriguingly, this transition is expressed in an increase of cooperativity in the temperature-dependent self-assembly mechanism, and more stable aggregates are obtained. 
For our study we select a benzene-1,3,5-tricarboxamide (BTA) core connected to a hydrophilic metal chelate via a hydrophobic, fluorinated L-phenylalanine based spacer (Scheme 1). The metal chelate selected is a Gd(III)-DTPA complex that contains two overall remaining charges per complex and necessarily two counter ions. The one-dimensional growth of the aggregate is directed by &pi;-&pi; stacking and intermolecular hydrogen bonding. However, the electrostatic, repulsive forces that arise from the charges on the Gd(III)-DTPA complex start limiting the one-dimensional growth of the BTA-based discotic once a certain size is reached. At millimolar concentrations the formed aggregate has a spherical shape and a diameter of around 5 nm as inferred from 1H-NMR spectroscopy, small angle X-ray scattering, and cryogenic transmission electron microscopy (cryo-TEM). The strength of the electrostatic repulsive interactions between molecules can be reduced by increasing the salt concentration of the buffered solutions. This screening of the charges induces a transition from spherical aggregates into elongated rods with a length &gt; 25 nm. Cryo-TEM allows to visualise the changes in shape and size. In addition, CD spectroscopy permits to derive the mechanistic details of the self-assembly processes before and after the addition of salt. Importantly, the cooperativity -a key feature that dictates the physical properties of the produced supramolecular polymers- increases dramatically upon screening the electrostatic interactions. This increase in cooperativity results in a significant increase in the molecular weight of the formed supramolecular polymers in water.

The goal of this experiment is to determine and control the size, shape and stability of self-assembled discotic amphiphiles in water. For aqueous based supramolecular polymers such level of control is very difficult. We apply a strategy using both repulsive and attractive interactions. The experimental techniques applied to characterize this system are broadly applicable.

控制的大小，形状和稳定性超分子聚合物在水

For aqueous based supramolecular polymers, the simultaneous control over shape, size and stability is very difficult1. At the same time, the ability to do ...

科研

工程学

Fabrication of Spatially Confined Complex Oxides

Complex materials such as high Tc superconductors, multiferroics, and colossal magnetoresistors have electronic and magnetic properties that arise from the inherent strong electron correlations that reside within them. These materials can also possess electronic phase separation in which regions of vastly different resistive and magnetic behavior can coexist within a single crystal alloy material. By reducing the scale of these materials to length scales at and below the inherent size of the electronic domains, novel behaviors can be exposed. Because of this and the fact that spin-charge-lattice-orbital order parameters each involve correlation lengths, spatially reducing these materials for transport measurements is a critical step in understanding the fundamental physics that drives complex behaviors. These materials also offer great potential to become the next generation of electronic devices 1-3. Thus, the fabrication of low dimensional nano- or micro-structures is extremely important to achieve new functionality. This involves multiple controllable processes from high quality thin film growth to accurate electronic property characterization. Here, we present fabrication protocols of high quality microstructures for complex oxide manganite devices. Detailed descriptions and required equipment of thin film growth, photo-lithography, and wire-bonding are presented.

We describe the use of pulsed laser deposition (PLD), photolithography and wire-bonding techniques to create micrometer scale complex oxides devices. The PLD is utilized to grow epitaxial thin films. Photolithography and wire-bonding techniques are introduced to create practical devices for measurement purposes.

空间密闭的复合氧化物制备

Complex materials such as high Tc superconductors, multiferroics, and colossal magnetoresistors have electronic and magnetic properties that arise from ...

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores

Developing low-cost, efficient processes for recovering and recycling palladium, gold and cobalt metals from urban mine remains a significant challenge in industrialized countries. Here, the development of optical mesosensors/adsorbents (MSAs) for efficient recognition and selective recovery of Pd(II), Au(III), and Co(II) from urban mine was achieved. A simple, general method for preparing MSAs based on using high-order mesoporous monolithic scaffolds was described. Hierarchical cubic Ia3d wagon-wheel-shaped MSAs were fabricated by anchoring chelating agents (colorants) into three-dimensional pores and micrometric particle surfaces of the mesoporous monolithic scaffolds. Findings show, for the first time, evidence of controlled optical recognition of Pd(II), Au(III), and Co(II) ions and a highly selective system for recovery of Pd(II) ions (up to ~95%) in ores and industrial wastes. Furthermore, the controlled assessment processes described herein involve evaluation of intrinsic properties (e.g., visual signal change, long-term stability, adsorption efficiency, extraordinary sensitivity, selectivity, and reusability); thus, expensive, sophisticated instruments are not required. Results show evidence that MSAs will attract worldwide attention as a promising technological means of recovering and recycling palladium, gold and cobalt metals.

Because of the importance and extensive use of palladium, gold and cobalt metals in high-technology equipment, their recovery and recycling constitute an important industrial challenge. The metal recovery system described herein is a simple, low-cost means for the effective detection, removal, and recovery of these metals from the urban mine.

检测和钯的回收，从城市矿山黄金和钴金属使用标有纳米货车车轮形的孔隙新型传感器/吸附剂

Developing low-cost, efficient processes for recovering and recycling palladium, gold and cobalt metals from urban mine remains a significant challenge in ...

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

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. Eye-tracking informatics are known to connect to a range of feelings, health conditions, mental states and actions. But because vision is the result of constant eye-movements, teasing out what is important from what is noise is complex and data intensive. Furthermore, a significant challenge is controlling for what people look at compared to what is presented to them.
The following presents a methodology for combining and analyzing eye-tracking on a video of a natural and complex scene with a machine learning technique for analyzing the content of the video. In the protocol we focus on analyzing data from filmed videos, how a video can be best used to record participants&#39; eye-tracking data, and importantly how the content of the video can be analyzed and combined with the eye-tracking data. We present a brief summary of the results and a discussion of the potential of the method for further studies in complex environments.

The objective of the protocol is to detail how to collect video data for use in the laboratory; how to record eye-tracking data of participants looking at the data and how to efficiently analyze the content of the videos that they were looking at using a machine learning technique.

将眼动追踪数据与从自由观看城市公园环境中的步行视频中获得的视频内容进行分析

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. ...

环境科学

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods often do not capture the richness inherent to the natural behavior. Here, we provide detailed step-by-step instructions with visualizations of our recent methodology, DeepBehavior. The DeepBehavior toolbox uses deep learning frameworks built with convolutional neural networks to rapidly process and analyze behavioral videos. This protocol demonstrates three different frameworks for single object detection, multiple object detection, and three-dimensional (3D) human joint pose tracking. These frameworks return cartesian coordinates of the object of interest for each frame of the behavior video. Data collected from the DeepBehavior toolbox contain much more detail than traditional behavior analysis methods and provides detailed insights to the behavior dynamics. DeepBehavior quantifies behavior tasks in a robust, automated, and precise way. Following the identification of behavior, post-processing code is provided to extract information and visualizations from the behavioral videos.

The purpose of this protocol is to utilize pre-built convolutional neural nets to automate behavior tracking and perform detailed behavior analysis. Behavior tracking can be applied to any video data or sequences of images and is generalizable to track any user-defined object.

用于自动行为分析的深入行为、深度学习工具箱的分步实施

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods ...

行为学

Deep Neural Networks for Image-Based Dietary Assessment

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a much simpler way, such as by taking an image with a smartphone camera. In this article, we will focus on such image-based approaches to dietary assessment. For the food image recognition problem, deep neural networks have achieved the state of the art in recent years, and we present our work in this field. In particular, we first describe the method for food and beverage image recognition using a deep neural network architecture, called NutriNet. This method, like most research done in the early days of deep learning-based food image recognition, is limited to one output per image, and therefore unsuitable for images with multiple food or beverage items. That is why approaches that perform food image segmentation are considerably more robust, as they are able to identify any number of food or beverage items in the image. We therefore also present two methods for food image segmentation - one is based on fully convolutional networks (FCNs), and the other on deep residual networks (ResNet).

The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image segmentation.

基于图像的饮食评估深度神经网络

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and ...

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Recently, deep learning-based segmentation models have been widely applied in the ophthalmic field. This study presents the complete process of constructing an orbital computed tomography (CT) segmentation model based on U-Net. For supervised learning, a labor-intensive and time-consuming process is required. The method of labeling with super-resolution to efficiently mask the ground truth on orbital CT images is introduced. Also, the volume of interest is cropped as part of the pre-processing of the dataset. Then, after extracting the volumes of interest of the orbital structures, the model for segmenting the key structures of the orbital CT is constructed using U-Net, with sequential 2D slices that are used as inputs and two bi-directional convolutional long-term short memories for conserving the inter-slice correlations. This study primarily focuses on the segmentation of the eyeball, optic nerve, and extraocular muscles. The evaluation of the segmentation reveals the potential application of segmentation to orbital CT images using deep learning methods.

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

An object segmentation protocol for orbital computed tomography (CT) images is introduced. The methods of labeling the ground truth of orbital structures by using super-resolution, extracting the volume of interest from CT images, and modeling multi-label segmentation using 2D sequential U-Net for orbital CT images are explained for supervised learning.

基于深度学习的医学图像分割在轨道计算机断层扫描 中的 应用

Recently, deep learning-based segmentation models have been widely applied in the ophthalmic field. This study presents the complete process of ...

<meta charset="utf8"></head><body>Swin-PSAxialNet：一种用于分割多个器官的简化方法

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Abdominal multi-organ segmentation is one of the most important topics in the field of medical image analysis, and it plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. In this study, an efficient multi-organ segmentation method called Swin-PSAxialNet based on the nnU-Net architecture is proposed. It was designed specifically for the precise segmentation of 11 abdominal organs in CT images. &#65279;The proposed network has made the following improvements compared to nnU-Net. Firstly, Space-to-depth (SPD) modules and parameter-shared axial attention (PSAA) feature extraction blocks were introduced, enhancing the capability of 3D image feature extraction. Secondly, a multi-scale image fusion approach was employed to capture detailed information and spatial features, improving the capability of extracting subtle features and edge features. Lastly, a parameter-sharing method was introduced to reduce the model's computational cost and training speed. &#65279;The proposed network achieves an average Dice coefficient of 0.93342 for the segmentation task involving 11 organs. Experimental results indicate the notable superiority of Swin-PSAxialNet over previous mainstream segmentation methods. The method shows excellent accuracy and low computational costs in segmenting major abdominal organs.

<meta charset="utf8"></head><body>Swin-PSAxialNet：一种高效的多器官分割技术

The present protocol describes an efficient multi-organ segmentation method called Swin-PSAxialNet, which has achieved excellent accuracy compared to previous segmentation methods. The key steps of this procedure include dataset collection, environment configuration, data preprocessing, model training and comparison, and ablation experiments.

用于复杂环境中显著目标检测的端到端深度神经网络

副本

摘要

探索更多视频

此视频中的章节

控制的大小，形状和稳定性超分子聚合物在水

空间密闭的复合氧化物制备

检测和钯的回收，从城市矿山黄金和钴金属使用标有纳米货车车轮形的孔隙新型传感器/吸附剂

将眼动追踪数据与从自由观看城市公园环境中的步行视频中获得的视频内容进行分析

用于自动行为分析的深入行为、深度学习工具箱的分步实施

基于图像的饮食评估深度神经网络

基于深度学习的医学图像分割在轨道计算机断层扫描中的应用

Swin-PSAxialNet：一种高效的多器官分割技术

Swin-PSAxialNet：一种高效的多器官分割技术

Swin-PSAxialNet：一种高效的多器官分割技术

用于复杂环境中显著目标检测的端到端深度神经网络

副本

摘要

探索更多视频

此视频中的章节

控制的大小，形状和稳定性超分子聚合物在水

空间密闭的复合氧化物制备

检测和钯的回收，从城市矿山黄金和钴金属使用标有纳米货车车轮形的孔隙新型传感器/吸附剂

将眼动追踪数据与从自由观看城市公园环境中的步行视频中获得的视频内容进行分析

用于自动行为分析的深入行为、深度学习工具箱的分步实施

基于图像的饮食评估深度神经网络

基于深度学习的医学图像分割在轨道计算机断层扫描 中的 应用

Swin-PSAxialNet：一种高效的多器官分割技术

Swin-PSAxialNet：一种高效的多器官分割技术

Swin-PSAxialNet：一种高效的多器官分割技术

基于深度学习的医学图像分割在轨道计算机断层扫描中的应用