Prediction of Uncertainty Estimation and Confidence Calibration Using Fully Convolutional Neural Network

Convolution neural networks(CNNs)have proven to be effective clinical imagingmethods.This study highlighted some of the key issues within these systems.It is difficult to train these systems in a limited clinical image databases,and many publications present strategies including such learning algorithm.Furthermore,these patterns are known formaking a highly reliable prognosis.In addition,normalization of volume and losses of dice have been used effectively to accelerate and stabilize the training.Furthermore,these systems are improperly regulated,resulting in more confident ratings for correct and incorrect classification,which are inaccurate and difficult to understand.This study examines the risk assessment of Fully Convolutional Neural Networks(FCNNs)for clinical image segmentation.Essential contributions have been made to this planned work:1)dice loss and cross-entropy loss are compared on the basis of segment quality and uncertain assessment of FCNNs;2)proposal for a group model for assurance measurement of full convolutional neural networks trained with dice loss and group normalization;And 3)the ability of the measured FCNs to evaluate the segment quality of the structures and to identify test examples outside the distribution.To evaluate the study’s contributions,it conducted a series of tests in three clinical image division applications such as heart,brain and prostate.The findings of the study provide significant insights into the predictive ambiguity assessment and a practical strategies for outside-distribution identification and reliable measurement in the clinical image segmentation.The approaches presented in this research significantly enhance the reliability and accuracy rating of CNNbased clinical imaging methods.

Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to establish an efficient intelligent identification model for pavement cracks.The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix.It has been widely used in geotechnical engineering,computer vision,medicine,and other fields.However,there are three major problems in the application of neural networks to crack identification.There are too few layers,extracted crack features are not complete,and the method lacks the efficiency to calculate the whole picture.In this study,a fully convolutional neural network based on ResNet-101 is used to establish an intelligent identification model of pavement crack regions.This method,using a convolutional layer instead of a fully connected layer,realizes full convolution and accelerates calculation.The region proposals come from the feature map at the end of the base network,which avoids multiple computations of the same picture.Online hard example mining and data-augmentation techniques are adopted to improve the model’s recognition accuracy.We trained and tested Concrete Crack Images for Classification(CCIC),which is a public dataset collected using smartphones,and the Crack Image Database(CIDB),which was automatically collected using vehicle-mounted charge-coupled device cameras,with identification accuracy reaching 91.4%and 86.4%,respectively.The proposed model has a higher recognition accuracy and recall rate than Faster RCNN and different depth models,and can extract more complete and accurate crack features in CIDB.We also analyzed translation processing,fuzzy,scaling,and distorted images.The proposed model shows a strong robustness and stability,and can automatically identify image cracks of different forms.It has broad application prospects in practical engineering problems.

Reconstructing the 3D digital core with a fully convolutional neural network

In this paper, the complete process of constructing 3D digital core by fullconvolutional neural network is described carefully. A large number of sandstone computedtomography (CT) images are used as training input for a fully convolutional neural networkmodel. This model is used to reconstruct the three-dimensional (3D) digital core of Bereasandstone based on a small number of CT images. The Hamming distance together with theMinkowski functions for porosity, average volume specifi c surface area, average curvature,and connectivity of both the real core and the digital reconstruction are used to evaluate theaccuracy of the proposed method. The results show that the reconstruction achieved relativeerrors of 6.26%, 1.40%, 6.06%, and 4.91% for the four Minkowski functions and a Hammingdistance of 0.04479. This demonstrates that the proposed method can not only reconstructthe physical properties of real sandstone but can also restore the real characteristics of poredistribution in sandstone, is the ability to which is a new way to characterize the internalmicrostructure of rocks.

基于LSTM-SAFCN模型的生物质锅炉NO_(x)排放浓度预测

针对生物质锅炉燃烧过程的动态特性,提出一种改进的长短期记忆-自注意力机制全卷积神经网络(LSTM-SAFCN)模型用于预测NO_(x)排放浓度。首先利用完全自适应噪声集合经验模态分解法(CEEMDAN)对数据进行预处理,消除数据噪声对NO_(x)排放浓度预测的影响;其次融合自注意力机制与长短时记忆-全卷积神经网络(LSTM-FCN)进行特征提取与预测建模,该拓展方法能够同时兼顾时间序列数据的局部细节与长期趋势特征;最后,利用生物质热电联产系统的实际运行数据验证了所提算法的有效性。

Accurate and Robust Eye Center Localization via Fully Convolutional Networks

Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network(FCN),which is an end-to-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by fine-tuning.Extensive experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.

Automated Delineation of Smallholder Farm Fields Using Fully Convolutional Networks and Generative Adversarial Networks

Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers,managers,and policymakers in terms of better managing and utilizing their agricultural resources.Due to their small size,irregular shape,and the use of mixed-cropping techniques,the farm fields of smallholder can be difficult to delineate automatically.In recent years,numerous studies on field contour extraction using a deep Convolutional Neural Network(CNN)have been proposed.However,there is a relative shortage of labeled data for filed boundaries,thus affecting the training effect of CNN.Traditional methods mostly use image flipping,and random rotation for data augmentation.In this paper,we propose to apply Generative Adversarial Network(GAN)for the data augmentation of farm fields label to increase the diversity of samples.Specifically,we propose an automated method featured by Fully Convolutional Neural networks(FCN)in combination with GAN to improve the delineation accuracy of smallholder farms from Very High Resolution(VHR)images.We first investigate four State-Of-The-Art(SOTA)FCN architectures,i.e.,U-Net,PSPNet,SegNet and OCRNet,to find the optimal architecture in the contour detection task of smallholder farm fields.Second,we apply the identified optimal FCN architecture in combination with Contour GAN and pixel2pixel GAN to improve the accuracy of contour detection.We test our method on the study area in the Sudano-Sahelian savanna region of northern Nigeria.The best combination achieved F1 scores of 0.686 on Test Set 1(TS1),0.684 on Test Set 2(TS2),and 0.691 on Test Set 3(TS3).Results indicate that our architecture adapts to a variety of advanced networks and proves its effectiveness in this task.The conceptual,theoretical,and experimental knowledge from this study is expected to seed many GAN-based farm delineation methods in the future.

ResCD-FCN:Semantic Scene Change Detection Using Deep Neural Networks

Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic(labels/categories)details before and after the timelines are analyzed.Periodical land change analysis is used for many real time applications for valuation purposes.Majority of the research works are focused on Convolutional Neural Networks(CNN)which tries to analyze changes alone.Semantic information of changes appears to be missing,there by absence of communication between the different semantic timelines and changes detected over the region happens.To overcome this limitation,a CNN network is proposed incorporating the Resnet-34 pre-trained model on Fully Convolutional Network(FCN)blocks for exploring the temporal data of satellite images in different timelines and change map between these two timelines are analyzed.Further this model achieves better results by analyzing the semantic information between the timelines and based on localized information collected from skip connections which help in generating a better change map with the categories that might have changed over a land area across timelines.Proposed model effectively examines the semantic changes such as from-to changes on land over time period.The experimental results on SECOND(Semantic Change detectiON Dataset)indicates that the proposed model yields notable improvement in performance when it is compared with the existing approaches and this also improves the semantic segmentation task on images over different timelines and the changed areas of land area across timelines.

基于R-FCN区域全卷积网络的绝缘子红外图像识别研究

红外热成像因其具有非接触性、灵敏性等优点,已被广泛应用于电力设备的带电检测及其诊断中。其中,对设备快速精确地识别定位是电力设备智能诊断的关键。然而利用传统机器算法对电力设备图像进行识别定位,存在泛化能力不强、鲁棒性较差等不足。针对此问题,开展基于R-FCN区域全卷积网络的绝缘子红外图像识别研究。在TensorFlow框架下搭建R-FCN检测模型,并利用迁移学习方法初始化模型权重,以提高训练效果。最后,将所研究算法与Faster-RCNN和SSD模型进行对比。实验表明,R-FCN模型的检测精度为89.2%,检测速度为23 fps,具有较高的精度和速度。该算法为绝缘子的智能诊断奠定坚实基础。

集成多种上下文与混合交互的显著性目标检测

显著性目标检测目的是识别和分割图像中的视觉显著性目标,它是计算机视觉任务及其相关领域的重要研究内容之一。当下基于全卷积网络(FCNs)的显著性目标检测方法已经取得了不错的性能,然而现实场景中的显著性目标类型多变且尺寸不固定,这使得准确检测并完整分割出显著性目标仍然是一个巨大的挑战。为此,该文提出集成多种上下文和混合交互的显著性目标检测方法,通过利用密集上下文信息探索模块和多源特征混合交互模块来高效预测显著性目标。密集上下文信息探索模块采用空洞卷积、不对称卷积和密集引导连接渐进地捕获具有强关联性的多尺度和多感受野上下文信息,通过集成这些信息来增强每个初始多层级特征的表达能力。多源特征混合交互模块包含多种特征聚合操作,可以自适应交互来自多层级特征中的互补性信息,以生成用于准确预测显著性图的高质量特征表示。此方法在5个公共数据集上进行了性能测试,实验结果表明,该文方法在不同的评估指标下与19种基于深度学习的显著性目标检测方法相比取得优越的预测性能。

局部聚类分析的FCN-CNN云图分割方法

空气中的尘埃、污染物及气溶胶粒子的存在严重影响了大气预测的有效性,毫米波雷达云图的有效分割成为解决这一问题的关键.提出了一种基于超像素分析的全卷积神经网路FCN和深度卷积神经网络CNN(FCNCNN)的云图分割方法.首先通过超像素分析对云图每个像素点的近邻域实现相应的聚类,同时将云图输入到不同步长的全卷积神经网络FCN 32s和FCN 8s中实现云图的预分割;FCN 32s预测结果中的"非云"区域一定是云图中的部分"非云"区域,FCN 8s预测结果中的"云"区域一定是云图中的部分"云"区域;余下的不确定的区域通过深度卷积神经网络CNN进行进一步分析.为提高效率,FCN-CNN选取了不确定区域中超像素的几个关键像素来代表超像素区域的特征,通过CNN网络来判断关键像素是"云"或者是"非云".实验结果表明,FCN-CNN的精度与MR-CNN、SP-CNN相当,但是速度相比于MR-CNN提高了880倍,相比于SP-CNN提高了1.657倍.

基于FCNN和ICAE的SAR图像目标识别方法

近年来,基于卷积神经网络(Convolutional Neural Network, CNN)的合成孔径雷达(Synthetic Aperture Radar, SAR)图像目标识别得到深入研究。全卷积神经网络(Fully Convolutional Neural Network, FCNN)是CNN结构上的改进,它比CNN能获得更高的识别率,但在训练过程中仍需要大量的带标签训练样本。该文提出一种基于FCNN和改进的卷积自编码器(Improved Convolutional Auto-Encoder, ICAE)的SAR图像目标识别方法,即先用ICAE无监督训练方式获得的编码器网络参数初始化FCNN的部分参数,后用带标签训练样本对FCNN进行训练。基于MSTAR数据集的十类目标分类实验结果表明,在不扩充带标签训练样本的情况下,该方法不仅能获得98.14%的平均正确识别率,而且具有较强的抗噪声能力。

基于FCN和互信息的医学图像配准技术研究

针对传统配准方法在进行三维多模态图像配准时存在收敛速度较慢、容易陷入极值等问题,提出一种基于全卷积神经网络(Fully Convolutional Networks,FCN)和互信息的配准方法。利用FCN模型提取二维图像深层特征并进行粗配准;将得到的配准结果作为互信息算法的初始搜索点,从而使搜索范围缩小至全局最优解附近;利用互信息算法对参数进一步微调优化,得到最优三维配准结果。实验结果表明,在进行CT-MR图像配准时,所提方法不仅可以大幅度提升配准速度,还能有效避免局部收敛的情况,具有更高的准确性。

融合VGG与FCN的智能出租车订单预测模型

为提高出租车市场管理和运营效率以及实现出租车效益最大化,在地图栅格化的基础上,提出一种融合VGG网络与全卷积网络(FCN)的出租车多区域订单预测模型。将出租车轨迹数据转换为订单图像,去除VGG网络全连接层仅保留主要结构以减少模型参数,利用该网络中深度卷积提取不同空间区域出租车行驶特征,使用FCN中反卷积层上采样重构下一个时间段出租车订单图像,从而获得不同区域和时间段的出租车订单预测数据,并以订单图像形式呈现在地图上。实验结果表明,与BP、RBF等预测模型相比,该模型预测结果平均准确率更高且均方根误差更低,可快速预测出租车多区域订单分布情况。

基于深度全卷积神经弹性网络WCGAN-GP模型的语音增强研究

Wasserstein距离生成对抗网络(Wasserstein Generative Adversal Network,WGAN)模型^([1])在语音增强中运用广泛,但存在梯度易爆炸、性能不稳定等问题。引入梯度惩罚(Gradient Penalty,GP)和弹性网络条件约束,并将生成器和判别器优化成深度全卷积神经网络(Deep Fully Convolutional Neural Networks,DFCNN)结构,提出一种基于DFCNN的弹性网络条件梯度惩罚(Wasserstein Conditional Generative Adversal Network Gradient Penalty,WCGAN-GP)模型。改进后的模型可以达到真实Lipschitz限制条件,提高了可控性、稳定性和特征提取能力,能更快优化训练。实验将改进后的模型与WGAN对不同噪声条件下的语音进行增强,结果证实了改进后的模型在语音增强方面的优越性。

基于FCN的眼底图像中央凹自动检测算法

针对传统算法很难识别彩色眼底图像中央凹的问题,提出一种基于全卷积网络(fully convolutional networks, FCN)的眼底图像中央凹自动检测算法.首先通过彩色眼底图像的局部上下文环境挖掘全局上下文信息,构建实现局部像素级分类的FCN模型,然后将局部像素级特征推广到全局金字塔池化模块中,使空间统计数据为全局语境理解提供了更好地描述与表达,从而有效获得了极具区分度的全局上下文信息,最后将全局与局部特征相融合,实现对中央凹的精准检测.实验结果表明,该算法提高了眼底暗病变检测的特异性,并为眼底严重病变的发现提供了有效证据.

Probability-Enhanced Anchor-Free Detector for Remote-Sensing Object Detection

Anchor-free object-detection methods achieve a significant advancement in field of computer vision,particularly in the realm of real-time inferences.However,in remote sensing object detection,anchor-free methods often lack of capability in separating the foreground and background.This paper proposes an anchor-free method named probability-enhanced anchor-free detector(ProEnDet)for remote sensing object detection.First,a weighted bidirectional feature pyramid is used for feature extraction.Second,we introduce probability enhancement to strengthen the classification of the object’s foreground and background.The detector uses the logarithm likelihood as the final score to improve the classification of the foreground and background of the object.ProEnDet is verified using the DIOR and NWPU-VHR-10 datasets.The experiment achieved mean average precisions of 61.4 and 69.0 on the DIOR dataset and NWPU-VHR-10 dataset,respectively.ProEnDet achieves a speed of 32.4 FPS on the DIOR dataset,which satisfies the real-time requirements for remote-sensing object detection.

Concurrent channel and spatial attention in Fully Convolutional Network for individual pig image segmentation

The separation of individual pigs from the pigpen scenes is crucial for precision farming,and the technology based on convolutional neural networks can provide a low-cost,non-contact,non-invasive method of pig image segmentation.However,two factors limit the development of this field.On the one hand,the individual pigs are easy to stick together,and the occlusion of debris such as pigpens can easily make the model misjudgment.On the other hand,manual labeling of group-raised pig data is time-consuming and labor-intensive and is prone to labeling errors.Therefore,it is urgent for an individual pig image segmentation model that can perform well in individual scenarios and can be easily migrated to a group-raised environment.In order to solve the above problems,taking individual pigs as research objects,an individual pig image segmentation dataset containing 2066 images was constructed,and a series of algorithms based on fully convolutional networks were proposed to solve the pig image segmentation problem.In order to capture the long-range dependencies and weaken the background information such as pigpens while enhancing the information of individual parts of pigs,the channel and spatial attention blocks were introduced into the best-performing decoders UNet and LinkNet.Experiments show that using ResNext50 as the encoder and Unet as the decoder as the basic model,adding two attention blocks at the same time achieves 98.30%and 96.71%on the F1 and IOU metrics,respectively.Compared with the model adding channel attention block alone,the two metrics are improved by 0.13%and 0.22%,respectively.The experiment of introducing channel and spatial attention alone shows that spatial attention is more effective than channel attention.Taking VGG16-LinkNet as an example,compared with channel attention,spatial attention improves the F1 and IOU metrics by 0.16%and 0.30%,respectively.Furthermore,the heatmap of the feature of different layers of the decoder after adding different attention information proves that with the increase of layers,the boundary of pig image segmentation is clearer.In order to verify the effectiveness of the individual pig image segmentation model in group-raised scenes,the transfer performance of the model is verified in three scenarios of high separation,deep adhesion,and pigpen occlusion.The experiments show that the segmentation results of adding attention information,especially the simultaneous fusion of channel and spatial attention blocks,are more refined and complete.The attention-based individual pig image segmentation model can be effectively transferred to the field of group-raised pigs and can provide a reference for its pre-segmentation.

图像分割算法在医学图像中的应用综述

医学图像分割是计算机辅助诊断领域的一项关键技术,其主要任务是将特定的器官、组织或异常区域从图像中准确地识别出来。但是医学图像的质量易受到其复杂纹理和成像设备限制(如噪声和边界不清晰)的影响,故传统的医学图像分割方法已难以满足现实临床需求。随着深度学习技术的进步,基于这一领域的算法已经取得了显著的进展。本文首先回顾了七种传统的医学图像分割策略,并重点介绍了两种当前主流的深度学习方法:全卷积神经网络和U-Net,最后文章探讨了目前深度学习技术所面临的挑战及其可能的解决策略。

光学遥感图像中舰船识别方法研究

光遥感图像舰船目标在检测识别过程中会存在诸多干扰,导致无法精准识别出舰船目标,对此,研究光学遥感图像中舰船识别方法。首先,在光学遥感图像内提取舰船目标显著性区域,抑制云雾、海杂波与海域陆地等背景信息对舰船目标识别的影响,完成光学遥感图像舰船目标的粗识别。然后,基于提取到的光学遥感图像显著性区域,利用CNN网络对其进行舰船目标精识别。实验结果表明,设计方法可以有效提取光学遥感图像的舰船目标显著性区域,并提取显著性区域的舰船目标特征;舰船目标识别精度始终高于95%,具有实用性。

DCS控制器中先导式泄压阀异常泄漏信号检测

在DCS控制器中,由于工作环境复杂,先导式泄压阀的泄漏往往呈渐进过程,其影响的电压信号表现为细微而持续的变化,传统方法基于静态或简单统计特征进行判断,难以捕捉因泄漏引起的微小动态变化,导致早期泄漏的漏检或误报。因此,提出一种针对DCS控制器中先导式泄压阀异常泄漏信号检测的新方法,对先导式泄压阀信号进行预处理消除噪声和干扰;应用变分模态分解(VMD)技术将预处理后的电压信号分解成多个本征模态函数(IMF)分量,揭示信号中不同频率段的特征,从而更容易捕捉泄漏引起的微小动态变化;从每个IMF分量中提取关键特征参数,并采用距离区分技术进行筛选,以确保所选特征对异常泄漏具有高敏感性和高区分度。设计并构建全卷积神经网络模型,将筛选出的特征参数输入该模型进行训练和学习,计算出该信号特征对应的异常泄漏概率,进而判断泄压阀是否存在异常泄漏,实现异常泄漏信号检测。实验结果表明:所提方法对电压信号分解准确率高,先导式泄压阀异常泄漏信号检测精度高。