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Detection of Left Ventricular Cavity from Cardiac MrI Images Using faster r-cnn

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Computers, Materials & Continua 2023年第1期74卷 1819-1835页

作者： Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil radzi Ambar Ahmed Abdu Alattab Anwar Ali Yahya Yousef Asiri Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu Pahat86400JohorMalaysia Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran61441Saudi Arabia Department of Computer Science Faculty of Computer Science and Info.SystemsThamar UniversityDhamar87246Yemen Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(Mr)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(rOI).The precise extraction of the LV’s rOI from cardiac MrI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(faster r-cnn)for the LV localization from short-axis cardiac MrI images using a region proposal network(rPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

关键词： Cardiac short-axis MrI images automatic left ventricle localization deep learning models faster r-cnn

Leguminous seeds detection based on convolutional neural networks:Comparison of faster r-cnn and YOLOv4 on a small custom dataset

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Artificial Intelligence in Agriculture 2023年第2期8卷 30-45页

作者： Noran S.Ouf Faculty of Engineering Cairo UniversityGizaEgypt

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 *** 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 experimental

关键词： Machine learning Object detection Leguminous seeds Deep learning Convolutional neural networks faster r-cnn YOLOv4

Detection of abnormal chicken droppings based on improved faster r-cnn

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International Journal of Agricultural and Biological Engineering 2023年第1期16卷 243-249页

作者： Min Zhou Junhui Zhu Zhihang Cui Hongying Wang Xianqiu Sun College of Engineering China Agricultural UniversityBeijing 100083China Shandong Minhe Pastoral Co. Yantai 265600ShandongChina

The characteristics of chicken droppings are closely related to the health of *** often judge the health of a chicken by looking at whether the chicken poop is *** present,the inspection of abnormal chicken droppings in chicken coops relies on manual observation,which is inefficient,accurate varies from person to person,labor-intensive,and has the risk of *** achieve efficient,accurate,and intelligent identification of abnormal chicken droppings,an abnormal chicken droppings detection method based on improved faster region-based Convolutional Neural Network(faster r-cnn)was proposed in this *** the feature extraction network stage,deformable convolution was used and combined with Path Augmentation-Feature Pyramid Network(PA-FPN)to improve the extraction ability of features at different *** the region Proposal Network(rPN)stage,the K-means++algorithm was used to cluster the dataset and obtain the Anchor-ratio which is more suitable for the chicken poop object,and the FocalLoss classification loss function was used to improve the classification ability of difficult *** the regional convolutional network stage,the region of interest calibration algorithm was used instead to obtain more accurate localization *** experimental results show that the improved faster r-cnn structure can reach an accuracy of 98.8%for abnormal chicken poop detection,and the average accuracy mean value was improved by 27.8%.The results can provide a key core technology support for establishing an efficient abnormal chicken droppings online detection system.

关键词： abnormal chicken droppings faster r-cnn detection non-destructive monitoring PA-FPN

Automatic real-Time Detection of Infant Drowning Using YOLOv5 and faster r-cnn Models Based on Video Surveillance

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Journal of Social Computing 2023年第1期4卷 62-73页

作者： Qianen He Zhiqiang Mei Huisheng Zhang Xiuying Xu School of Physics and Information Engineering Fuzhou UniversityFuzhou 350108China

Infant drowning has occurred frequently in swimming pools recent years,which motivates the research on automatic real-time detection of the *** youths or adults,swimming infants are small in terms of size and motion range,and unable to send out distress signals in emergencies,which exerts negative effects on the detection of *** at this problem,a new step is initialized towards detecting infant drowning automatically and efficiently based on video *** live-scene videos of infant swimming and drowning are collected from a variety of natatoriums and labeled as datasets.A part of the datasets is downscaled or enlarged to enhance generalization ability of the *** this basis,advantages of faster r-cnn and a series of YOLOv5 models are specifically explored to enable fast and accurate detection of infant drowning in *** learning experiments are carried out,model test results show that mean Average Precision(mAP)of either faster r-cnn or YOLOv5s of the series of YOLOv5 can be over 89%;the former can process merely 6 frames of videos per second with the precision of only 62.04%,while the latter can reach an average speed of 75 frames/s with the precision of about 86.6%.The YOLOv5s eventually stands out as an optimal model for detecting infant drowning in view of comprehensive performance,which is of great application value to reduce the accidents in swimming pools.

关键词： infant drowning detection YOLOv5 faster r-cnn video surveillance supervised learning

An Accurate and real-time Method of Self-blast Glass Insulator Location Based on faster r-cnn and U-net with Aerial Images

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CSEE Journal of Power and Energy Systems 2019年第4期5卷 474-482页

作者： Zenan Ling Dongxia Zhang robert C.Qiu Zhijian Jin Yuhang Zhang Xing He Haichun Liu Department of Electrical Engineering Center for Big Data and Artificial IntelligenceState Energy Smart Grid Research and Development CenterShanghai Jiao Tong UniversityShanghai 200240China China Electric Power research Institute Beijing 100192China Department of Electrical and Computer Engineering Tennessee Technological UniversityCookevilleTN 38505USA

This paper proposes a new deep learning framework for the location of broken insulators(in particular the self-blast glass insulator)in aerial *** address the broken insulators location problem in a low signal-noise-ratio(SNr)*** deal with two modules:1)object detection based on faster r-cnn,and 2)classification of pixels based on *** the first time,our paper combines the above two *** combination is motivated as follows:faster r-cnn is used to improve SNr,while the U-net is used for classification of pixels.A diverse aerial image set measured by a power grid in China is tested to validate the proposed ***,a comparison is made among different methods and the result shows that our approach is accurate in real time.

关键词： Aerial images deep learning faster r-cnn insulators location real-time U-net

基于faster r-cnn与形态法的路面病害识别

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中国公路学报 2021年第9期34卷 181-193页

作者：晏班夫徐观亚栾健林杜邓露湖南大学土木工程学院湖南长沙410082 湖南大学风工程与桥梁工程湖南省重点实验室湖南长沙410082 中南林业科技大学材料科学与工程学院湖南长沙410004 云南航天工程物探检测股份有限公司云南昆明650217

为提高基于图像处理的路面表观病害检测识别效率及精度,引入目标检测中的快速区域卷积神经网络(faster region Convolutional Neural Network,faster r-cnn)算法以快速识别病害种类、位置与面积;针对已提取的带边框裂缝病害区域,采用基... 详细信息

为提高基于图像处理的路面表观病害检测识别效率及精度,引入目标检测中的快速区域卷积神经网络(faster region Convolutional Neural Network,faster r-cnn)算法以快速识别病害种类、位置与面积;针对已提取的带边框裂缝病害区域,采用基于VGG16迁移学习与模型微调的cnn与50%重叠率的滑动窗口定位裂缝骨架,进而利用形态法操作提取裂缝形态,计算其长度与宽度;针对faster r-cnn算法在病害种类识别时漏检率低但误检率偏高的问题,引入精确率、召回率和F1分数指标对算法进行评估,并根据F1分数最大值确定相应的病害框像素面积及置信度阈值来降低误检率,以适应路面表观病害多样化的应用场景。运用开发的病害识别算法对广东一高速公路路面进行表观检测。结果表明:所提方法对典型裂缝图片的识别效率及精度均高于单独应用cnn滑动窗口和传统形态法的全局图像处理方法;对分段的裂缝边界框进行合并,且病害框像素面积及置信度阈值取优化值后,横向裂缝精确率由合并前的0.861提升至合并后的0.918,横向及纵向裂缝误检率则分别由调整前的20.4%和23.8%下降至调整后的8.2%和6.9%,漏检率则稍有提高。基于faster r-cnn、cnn及形态法的路面病害识别方法具有工作高效、漏检率低的优点,在引入评估指标、最优病害框像素面积与置信度阈值后,病害误检率也大幅降低,具有潜在工程应用价值。

关键词：道路工程路面 faster r-cnn 病害识别形态法

融合自适应注意力机制的faster r-cnn目标检测算法

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激光与光电子学进展 2022年第12期59卷 381-386页

作者：王启胜王凤随陈金刚刘芙蓉安徽工程大学电气工程学院安徽芜湖241000 检测技术与节能装置安徽省重点实验室安徽芜湖241000 高端装备先进感知与智能控制教育部重点实验室安徽芜湖241000

针对faster r-cnn目标检测算法存在的定位和检测精度问题,设计了一种可嵌入faster r-cnn目标检测算法并进行端到端训练的可移动的注意力(MA)模型。首先,为了获取更加精确的空间位置信息,MA采用两个自适应最大池化分别基于输入特征图的... 详细信息

针对faster r-cnn目标检测算法存在的定位和检测精度问题,设计了一种可嵌入faster r-cnn目标检测算法并进行端到端训练的可移动的注意力(MA)模型。首先,为了获取更加精确的空间位置信息,MA采用两个自适应最大池化分别基于输入特征图的水平和竖直两个方向进行特征聚合,生成两个独立的方向感知特征图;其次,为了防止模型过拟合,使用Sigmoid激活函数增加网络非线性;最后,为了充分利用已经得到的空间位置信息,将具有非线性的两个特征图与输入特征图依次相乘以增强输入特征图的表征能力。实验结果表明:基于MA改进的faster r-cnn目标检测算法有效地提升了网络对感兴趣目标的定位能力,并且平均检测精度也得到了明显的提升。

关键词：机器视觉目标检测 faster r-cnn 注意力机制卷积神经网络 resNet-50

基于改进faster r-cnn的SAr图像飞机检测算法

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北京航空航天大学学报 2021年第1期47卷 159-168页

作者：李广帅苏娟李义红火箭军工程大学核工程学院西安710025

在合成孔径雷达(SAr)图像分析领域,飞机作为一种重要目标,对其的检测越来越受到重视。针对传统SAr图像飞机检测算法需要人工设计特征且鲁棒性较差的问题,提出了一种基于改进faster r-cnn的SAr图像飞机检测算法。制作了一个SAr图像飞机... 详细信息

在合成孔径雷达(SAr)图像分析领域,飞机作为一种重要目标,对其的检测越来越受到重视。针对传统SAr图像飞机检测算法需要人工设计特征且鲁棒性较差的问题,提出了一种基于改进faster r-cnn的SAr图像飞机检测算法。制作了一个SAr图像飞机数据集(SAD),以faster r-cnn为检测框架,利用改进k-means算法设计更合理的先验锚点框,以适应飞机目标的形状特点;借鉴inception模块思想,设计多路不同尺寸卷积核以扩展网络宽度,增强对浅层特征的表达;分析残差网络Layer5层的特征输出具有更大的感受野,对其上采样后进行特征融合以利用更多的上下文信息;同时引入Mask r-cnn算法中提出的roI Align单元,消除特征图与原始图像的映射偏差。实验结果表明:相比原始的faster r-cnn算法,所提改进的faster r-cnn算法在SAr图像飞机数据集上平均检测精度提高了7.4%,同时保持了较快的检测速度。

关键词：飞机检测 faster r-cnn 浅层特征增强上下文信息 roI Align

基于伪样本正则化faster r-cnn的交通标志检测

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吉林大学学报（工学版） 2021年第4期51卷 1251-1260页

作者：李厚杰王法胜贺建军周瑜李威窦宇轩大连民族大学信息与通信工程学院辽宁大连116605 大连民族大学计算机科学与工程学院辽宁大连116605

针对交通标志检测算法往往仅能对特定类标志检测或基于深度学习方法因训练样本少而造成“过拟合”高风险等问题,本文提出了一种基于伪样本正则化faster r-cnn深度学习的标志检测算法。该算法首先通过训练数据集提供的标志和无标注的背... 详细信息

针对交通标志检测算法往往仅能对特定类标志检测或基于深度学习方法因训练样本少而造成“过拟合”高风险等问题,本文提出了一种基于伪样本正则化faster r-cnn深度学习的标志检测算法。该算法首先通过训练数据集提供的标志和无标注的背景样本,提出了一种伪样本正则化策略。然后,通过深度学习模型中区域建议生成网络获取建议区域。最后,利用交替训练策略、共享cnn策略和联合训练策略、rPN网络和Fast r-cnn目标检测网络交替训练和联合训练,最终获取faster r-cnn交通标志检测模型,实现了各类标志的检测,并有效降低了“过拟合”风险。实验结果验证了本文算法的有效性。

关键词：信息处理技术交通标志检测伪样本正则化 faster r-cnn 区域建议网络