Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network

The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.

Diagnosis of Middle Ear Diseases Based on Convolutional Neural Network

An otoscope is traditionally used to examine the eardrum and ear canal.A diagnosis of otitis media(OM)relies on the experience of clinicians.If an examiner lacks experience,the examination may be difficult and time-consuming.This paper presents an ear disease classification method using middle ear images based on a convolutional neural network(CNN).Especially the segmentation and classification networks are used to classify an otoscopic image into six classes:normal,acute otitis media(AOM),otitis media with effusion(OME),chronic otitis media(COM),congenital cholesteatoma(CC)and traumatic perforations(TMPs).The Mask R-CNN is utilized for the segmentation network to extract the region of interest(ROI)from otoscopic images.The extracted ROIs are used as guiding features for the classification.The classification is based on transfer learning with an ensemble of two CNN classifiers:EfficientNetB0 and Inception-V3.The proposed model was trained with a 5-fold cross-validation technique.The proposed method was evaluated and achieved a classification accuracy of 97.29%.

Hybrid Convolutional Neural Network for Plant Diseases Prediction

Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural products.The monitoring of plant health continuously and detecting the diseases is a significant for sustainable agri-culture.Manual system to monitor the diseases in plant is time consuming and report a lot of errors.There is high demand for technology to detect the plant dis-eases automatically.Recently image processing approach and deep learning approach are highly invited in detection of plant diseases.The diseases like late blight,bacterial spots,spots on Septoria leaf and yellow leaf curved are widely found in plants.These are the main reasons to affects the plants life and yield.To identify the diseases earliest,our research presents the hybrid method by com-bining the region based convolutional neural network(RCNN)and region based fully convolutional networks(RFCN)for classifying the diseases.First the leaf images of plants are collected and preprocessed to remove noisy data in image.Further data normalization,augmentation and removal of background noises are done.The images are divided as testing and training,training images are fed as input to deep learning architecture.First,we identify the region of interest(RoI)by using selective search.In every region,feature of convolutional neural network(CNN)is extracted independently for further classification.The plants such as tomato,potato and bell pepper are taken for this experiment.The plant input image is analyzed and classify as healthy plant or unhealthy plant.If the image is detected as unhealthy,then type of diseases the plant is affected will be displayed.Our proposed technique achieves 98.5%of accuracy in predicting the plant diseases.

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based onMulti-Scale and Multi Feature Convolution Neural Network

There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components showa continuous and stable charging and discharging state,a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed.Firstly,a voltage stability analysis model based onmulti-scale and multi feature convolution neural network is constructed,and the multi-scale and multi feature convolution neural network is optimized based on Self-OrganizingMaps(SOM)algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility.According to the optimal scheduling objectives and network size,the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales;Finally,the time series characteristics of regional power grid load and distributed generation are analyzed.According to the regional hierarchical time-sharing configuration model of“cloud”,“group”and“end”layer,the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized.The experimental results show that after applying this algorithm,the best grid side distributed energy storage configuration scheme can be determined,and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved.

Facial Expression Recognition Using Enhanced Convolution Neural Network with Attention Mechanism

Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images.

基于改进Faster R-CNN的高铁扣件弹条缺陷检测

针对复杂光照环境导致的高铁扣件弹条缺陷检测困难问题,提出一种基于改进Faster R-CNN的弹条缺陷检测方法。通过多层卷积神经网络提取缺陷特征图,提高网络对缺陷特征的关注程度,降低对复杂光照环境干扰的影响;设计区域候选网络生成候选区域,并根据候选区域进行池化,在特征图中提取相对应的具体缺陷位置;利用区域候选网络的全连接网络层计算获得缺陷的具体类别与精确位置,得到最终的检测结果。所提算法可充分抑制光照环境干扰影响,显著增强缺陷特征的表征能力;简化了图像预处理环节,降低了对原始图像成像质量的要求。实验结果表明,所提算法能够实现对高铁扣件弹条缺陷的有效检测。与现有算法相比,具有较高的精确度和较强的鲁棒性,运算效率也得到显著提升。

基于改进Faster R-CNN与U-Net算法的桥梁病害识别与量化方法

为实现桥梁病害检测的自动化,对基于图像处理技术的混凝土桥梁表观病害的智能识别和尺寸确定方法展开研究.提出基于改进Faster R-CNN算法的病害识别方法,利用K均值聚类和遗传算法对区域候选网络锚框进行优化设计;以裂缝预测区域为基础,提出ResNet34结合U-Net的裂缝形态提取方法,并结合裂缝形态学研究了裂缝像素宽度和长度的确定方法.结果表明:锚框优化设计可改进Faster R-CNN算法的表观病害识别效果,5类常见病害的预测准确率、召回率、平均精确率分别由68.40%、69.87%、74.64%提升到85.40%、83.59%、83.72%;利用病害预测框,结合改进U-Net算法的裂缝像素尺寸计算,可实现裂缝病害尺寸的自动测量;基于改进Faster R-CNN和改进U-Net的方法可实现混凝土桥梁常见病害的智能识别和尺寸量化,从而提高桥梁病害检测效率并促进桥梁技术状况评定的智能化.

复杂背景下基于改进Mask R-CNN的路面裂缝检测算法

裂缝检测对路面养护具有重要意义,深度学习在该领域取得一定成效。然而,在实际应用中,图像中的噪声纹理背景、复杂的裂缝拓扑结构和图像采集设备给裂缝检测带来了一定的挑战。为了提升在复杂场景下的路面裂缝检测精度,提出了一种改进掩码区域卷积神经网络(Mask R-CNN)模型的实例分割算法。使用ConvNeXt-T替代Mask R-CNN的ResNet50框架作为特征生成网络,在自下而上捕获长期依赖的同时保持裂缝特征多样性;设计高维特征提取模块(HFEM)获取高级语义信息,消除背景噪声;引入感受野模块(RFB),扩大感受野,增强多尺度特征信息交互能力。在多结构裂缝图像(MSCI)数据集上进行对比实验,结果表明,提出的改进方法能显著提升Mask R-CNN模型的分割精度,优于经典的Cascade Mask RCNN,最佳模型F1得分84.15%,相较原算法提高了6.29%。在DeepCrack数据集上进行泛化性实验,表现优异。

改进Mask R-CNN的无人机影像建筑物提取

从无人机影像中自动提取建筑物对城乡规划和管理至关重要,然而,在复杂背景干扰和建筑物外观变化很大的情况下给实例提取带来挑战。因此,提出一种改进的Mask区域卷积神经网络(R-CNN)方法用于无人机影像的建筑物自动实例提取。改进方法以ResNet-101作为特征提取网络,在特征融合网络方面,通过添加自底向上的路径增强整个特征层次的定位能力,同时在特征融合中加入空洞空间金字塔池化模块(ASPP)来提高多尺度能力与改善模型性能。在自制建筑物数据集上的综合实验结果表明,与原始的Mask R-CNN方法相比,改进方法的mAP值提高了2.6%,能够很好地实现无人机影像建筑物实例提取。

基于改进Mask R-CNN的输电线路安全检测方法研究

随着全球电力需求的持续增长和电力网络的不断扩展,输电线路的安全性与稳定性尤为重要。输电线路在连接发电厂和用户的过程中,承担着可靠输送电能的重要职责。为提升输电线路的安全,研究提出一种基于掩膜区域卷积神经网络(Mask Region Convolutional Neural Network,Mask R-CNN)的输电线路安全检测模型,并引入特征金字塔网络(Feature Pyramid Network,FPN)对其进行改进。实验结果表明,在数据集尺寸为500时,改进Mask R-CNN模型的准确率为0.91,损失函数值为0.01。改进的Mask R-CNN模型能够有效提升输电线路缺陷检测的精度,具有较高的实用价值,能够提高电力系统的安全监控水平。

基于CA-FasterR-CNN的甲骨文原始拓片单字分割方法

甲骨文拓片经过长时间的埋藏和侵蚀,变得形态复杂,字体模糊,单字之间缺乏明确的分隔,这给甲骨文识别带来了极大的困难。基于此,本文提出了一种基于坐标注意力机制的快速区域卷积神经网络(Coordinate Attention Mechanism-based Faster Region Convolutional Neural Network,CA-Faster R-CNN)模型以实现对甲骨文拓片图像中的单字分割。通过坐标通道注意力机制的引入,模型能够更加关注甲骨文字形特征,从而提升了对甲骨文图像细节的捕捉能力,最后训练结果框线与标准框线基本重合,证明模型分割效果良好。

Localization and Classification of Rice-grain Images Using Region Proposals-based Convolutional Neural Network

This paper proposes a solution to localization and classification of rice grains in an image.All existing related works rely on conventional based machine learning approaches.However,those techniques do not do well for the problem designed in this paper,due to the high similarities between different types of rice grains.The deep learning based solution is developed in the proposed solution.It contains pre-processing steps of data annotation using the watershed algorithm,auto-alignment using the major axis orientation,and image enhancement using the contrast-limited adaptive histogram equalization(CLAHE)technique.Then,the mask region-based convolutional neural networks(R-CNN)is trained to localize and classify rice grains in an input image.The performance is enhanced by using the transfer learning and the dropout regularization for overfitting prevention.The proposed method is validated using many scenarios of experiments,reported in the forms of mean average precision(mAP)and a confusion matrix.It achieves above 80%mAP for main scenarios in the experiments.It is also shown to perform outstanding,when compared to human experts.

Leguminous seeds detection based on convolutional neural networks:Comparison of Faster R-CNN and YOLOv4 on a small custom dataset

This paper help with leguminous seeds detection and smart farming. There are hundreds of kinds of seeds and itcan be very difficult to distinguish between them. Botanists and those who study plants, however, can identifythe type of seed at a glance. As far as we know, this is the first work to consider leguminous seeds images withdifferent backgrounds and different sizes and crowding. Machine learning is used to automatically classify andlocate 11 different seed types. We chose Leguminous seeds from 11 types to be the objects of this study. Thosetypes are of different colors, sizes, and shapes to add variety and complexity to our research. The images datasetof the leguminous seeds was manually collected, annotated, and then split randomly into three sub-datasetstrain, validation, and test (predictions), with a ratio of 80%, 10%, and 10% respectively. The images consideredthe variability between different leguminous seed types. The images were captured on five different backgrounds: white A4 paper, black pad, dark blue pad, dark green pad, and green pad. Different heights and shootingangles were considered. The crowdedness of the seeds also varied randomly between 1 and 50 seeds per image.Different combinations and arrangements between the 11 types were considered. Two different image-capturingdevices were used: a SAMSUNG smartphone camera and a Canon digital camera. A total of 828 images wereobtained, including 9801 seed objects (labels). The dataset contained images of different backgrounds, heights,angles, crowdedness, arrangements, and combinations. The TensorFlow framework was used to construct theFaster Region-based Convolutional Neural Network (R-CNN) model and CSPDarknet53 is used as the backbonefor YOLOv4 based on DenseNet designed to connect layers in convolutional neural. Using the transfer learningmethod, we optimized the seed detection models. The currently dominant object detection methods, Faster RCNN, and YOLOv4 performances were compared experimentally. The mAP (mean average precision) of the FasterR-CNN and YOLOv4 models were 84.56% and 98.52% respectively. YOLOv4 had a significant advantage in detection speed over Faster R-CNN which makes it suitable for real-time identification as well where high accuracy andlow false positives are needed. The results showed that YOLOv4 had better accuracy, and detection ability, as wellas faster detection speed beating Faster R-CNN by a large margin. The model can be effectively applied under avariety of backgrounds, image sizes, seed sizes, shooting angles, and shooting heights, as well as different levelsof seed crowding. It constitutes an effective and efficient method for detecting different leguminous seeds incomplex scenarios. This study provides a reference for further seed testing and enumeration applications.

基于改进Fast R-CNN的红外图像行人检测研究

针对红外图像行人检测任务中行人细节信息少,特征提取计算量大以及易受背景影响等问题,提出了一种改进的Fast R-CNN(快速区域卷积神经网络)红外图像行人检测方法。改进主要涉及两个方面:(1)结合红外图像的特点提出了一种自适应ROI提取算法,在不影响检测准确率的前提下,降低了ROI数量,使得网络的计算量减小;(2)提出了一种加权锚点框的定位机制,基于3种不同宽高比锚点框的检测置信度进行坐标加权,获得更准确的定位框。实验结果表明,本文提出的改进方法与传统的Haar+LBP+HOG+SVM算法及Fast R-CNN算法相比,红外图像行人检测的准确率从80.3%和91.2%提高到92.3%,检测速度从68 ms/f和25 ms/f提高到12 ms/f,提高了系统的性能。

改进的Faster R-CNN方法及其在电缆隧道积水定位识别中的应用

针对电缆隧道内积水的问题,提出了一种改进的基于区域建议的卷积神经网络(FasterR-CNN)方法,并将其应用在电缆隧道积水定位识别中。考虑到Softmax分类方法的正则化参数选取会引起概率计算产生问题,改用支持向量机(SVM)进行图像分类,以增强分类的置信度。使用区域建议网络(RPN)提取隧道积水原图中的区域建议,然后用FastR-CNN检测网络在建议框中进行图像识别、SVM分类和位置精修。实验结果表明,所提方法计算速度快、识别精度高,在实际工程中表现出较高的效率。

基于Faster R-CNN的除草机器人杂草识别算法

针对当前除草机器人杂草识别定位不准确、实时性差等问题,提出一种基于Faster R-CNN的草坪杂草识别算法。该方法首先使用快速区域卷积神经网络(Faster R-CNN)算法训练初始化模型,然后通过在网络池化层后添加生成对抗网络(GAN)噪声层来提高网络的鲁棒性。试验结果表明,该种方法在正常拍摄的测试集图片中识别率达到97.05%,在加噪图片测试集的识别率达到95.15%,识别结果均优于传统的机器学习方法。同时,本方法具有识别速度快的特点,可用于实时检测,在园林杂草清理等方面具有应用价值。

基于改进Faster R-CNN的无人机视频车辆自动检测

为了从广域视角准确提取道路交通信息,提出了一种用于无人机视频车辆自动识别的改进Faster R-CNN模型.该模型以基于ZF网络的Faster R-CNN为原型,优化调整学习策略、训练图像尺寸、学习率等模型参数,调整RPN网络卷积核并引入SoftNMS算法,增加1~3个特征提取卷积层和激活层.基于无人机交通视频构建了训练图像集,对现有Faster R-CNN模型及改进模型进行训练和测试.结果显示,与采用Step学习策略的模型相比,采用学习策略Inv的模型车辆识别平均准确率提高了0.4%~9.4%.引入SoftNMS算法的模型比引入前的模型平均准确率提高了0.1%~7.9%.提出的改进模型平均准确率为94.6%,较基于ZF的Faster R-CNN模型、基于VGGM的Faster R-CNN模型和基于VGG16的Faster R-CNN模型分别提高了13.1%、13.1%和4.1%,且训练时间减少约3%,对多种场景的视频车辆检测具有较好的适用性.

基于改进Faster R-CNN的铁路客车螺栓检测研究

为确保铁路客车运行安全,提出一种基于快速区域卷积神经网络(Faster R-CNN)目标检测的客车关键部件图像缺陷检测算法,针对算法在小尺度螺栓检测方面存在的问题提出2点改进,首先,结合深度残差网络和Inception网络两者优点替换原VGG16网络,并增加上采样层,解决图像经过卷积网络特征信息流失严重的问题;其次,通过K-means++聚类算法优化区域建议网络(RPN)中锚点的尺寸和比例,提高生成建议区域的精确性,解决缺陷目标定位不准确的问题;最后,用创建的螺栓缺陷数据集进行对比验证。结果表明:改进后的算法检测准确率可达87.4%,相较原算法提高8.9%,且对于多目标缺陷与混淆目标,漏检率与误检率分别降低9.9%和11%。

Faster R-CNN模型在车辆检测中的应用

针对传统机器学习方法在车辆检测应用中易受光照、目标尺度和图像质量等因素影响,效率低下且泛化能力较差的问题,提出一种基于改进的较快的基于区域卷积神经网络(R-CNN)模型的车辆检测方法。该方法以Faster R-CNN模型为基础,通过对输入图像进行卷积和池化等操作提取车辆特征,结合多尺度训练和难负样本挖掘策略降低复杂环境的影响,利用KITTI数据集对深度神经网络模型进行训练,并采集实际场景中的图像进行测试。仿真实验中,在保证检测时间的情况下,相对原Faster R-CNN算法检测精确度提高了约8%。实验结果表明,所提方法能够自动地提取车辆特征,解决了传统方法提取特征费时费力的问题,同时提高了车辆检测精确度,具有良好的泛化能力和适用范围。

基于Mask R-CNN的柑橘主叶脉显微图像实例分割模型

针对目前植物解剖表型的测量与分析过程自动化低,难以应对复杂解剖表型的提取和识别的问题,以柑橘主叶脉为研究对象,提出了一种基于掩膜区域卷积神经网络(Mask region convolutional neural network,Mask R-CNN)的主叶脉显微图像实例分割模型,以残差网络ResNet50和特征金字塔(Feature pyramid network,FPN)为主干特征提取网络,在掩膜(Mask)分支上添加一个新的感兴趣区域对齐层(Region of interest Align,RoI-Align),提升Mask分支的分割精度。结果表明,该网络架构能够精准地对柑橘主叶脉横切面中的髓部、木质部、韧皮部和皮层细胞进行识别分割。Mask R-CNN模型对髓部、木质部、韧皮部和皮层细胞的分割平均精确率(交并比(IoU)为0.50)分别为98.9%、89.8%、95.7%和97.2%,对4个组织区域的分割平均精确率均值(IoU为0.50)为95.4%。与未在Mask分支添加RoI-Align的Mask R-CNN相比,精度提升1.6个百分点。研究结果表明,Mask R-CNN模型对柑橘主叶脉各类组织区域具有良好的识别分割效果,可为柑橘微观表型研究提供技术支持与研究基础。