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.

基于改进Faster R-CNN的变电站设备外部缺陷检测

针对变电站设备外部缺陷目标检测任务中目标形状多样,周围环境复杂,当前代表性算法识别准确度低,错检漏检严重的问题,对比了众多目标检测算法在变电站设备缺陷数据集上的检测结果,检测精度较高的是添加了特征融合金字塔结构的Faster R-CNN(faster region-based convolutional network)算法,但其对小目标物体和设备渗漏油的检测精度仍有提升空间,为此设计一种基于Faster R-CNN的改进算法。改进算法通过对输入图像进行数据增强,在网络中添加SPP(spatial pyramid pooling)结构以及改进特征融合方式,对分类以及边界框回归损失函数进行改进的方式来提高缺陷的检测精度。与原Faster R-CNN算法进行对比,改进算法在变电站设备缺陷目标检测数据集的检测结果中AP(average precision)(0.5∶0.95)提高了2.7个百分点,AP(0.5)提高了4.3个百分点,对小目标物体的检测精度也提高了1.8个百分点,试验结果验证了该方法的有效性。

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.

多尺度特征和极化自注意力的Faster-RCNN水漂垃圾识别

针对小目标水漂垃圾形态多变、分辨率低且信息有限,导致检测效果不理想的问题,提出一种改进的Faster-RCNN(Faster Regions with Convolutional Neural Network)水漂垃圾检测算法MP-Faster-RCNN(Faster-RCNN with Multi-scale feature and Polarized self-attention)。首先,建立黄河兰州段小目标水漂垃圾数据集,将空洞卷积结合ResNet-50代替原来的VGG-16(Visual Geometry Group 16)作为主干特征提取网络,扩大感受野以提取更多小目标特征;其次,在区域生成网络(RPN)利用多尺度特征,设置3×3和1×1的两层卷积,补偿单一滑动窗口造成的特征丢失;最后,在RPN前加入极化自注意力,进一步利用多尺度和通道特征提取更细粒度的多尺度空间信息和通道间依赖关系,生成具有全局特征的特征图,实现更精确的目标框定位。实验结果表明,MP-Faster-RCNN能有效提高水漂垃圾检测精度,与原始Faster-RCNN相比,平均精度均值(mAP)提高了6.37个百分点,模型大小从521 MB降到了108 MB,且在同一训练批次下收敛更快。

基于Faster R-CNN的密集人群检测算法

为提高拥挤场景下的人群检测准确率,提出一种基于改进Faster R-CNN的密集人群检测算法。首先,在特征提取阶段添加空间与通道注意力机制,使用加强的双向特征金字塔网络(S-BiFPN)替代原网络中的多尺度特征金字塔(FPN),使网络对重要特征进行自主学习并加强对图像深层特征的提取;其次,引入多实例预测(MIP)算法对实例进行预测,以避免模型对拥挤场景下的目标造成漏检;最后,对模型中的非极大值抑制(NMS)进行优化,并额外增设一个交并比(IoU)阈值,以对检测结果的干扰项进行精确抑制。在开源的密集人群检测数据集上进行测试的结果显示,相较于原Faster R-CNN算法,所提算法的平均精度(AP)提升5.6%,Jaccard指数值提升3.2%。所提算法具有较高检测精度和稳定性,可以满足密集场景人群检测的需求。

基于改进Faster R-CNN算法的行人识别系统设计与研究

文章基于改进更快的区域卷积神经网络(Faster Region Convolutional Neural Networks,Faster R-CNN)模型,提出了一种行人识别系统设计。介绍了计算机视觉常用技术手段与方法、通行检测步骤,分析了主流的算法优缺点,利用深度学习方法提取图像特征,然后使用改进Faster R-CNN模型进行目标检测。在改进Faster R-CNN模型中,采用了自适应尺度池化和增强的感兴趣区域(Region of Interest,RoI)池化技术,可以提高模型检测精度和速度。

基于Faster R-CNN的人脸面部情感识别方法

常规人脸面部情感识别方法不准确,存在识别后的情感反馈误差大的问题,为此提出基于更快的区域卷积神经网络(Faster Region-Convolutional Neural Network,Faster R-CNN)的人脸面部情感识别方法。首先,采集人脸图像数据,通过面部检测、面部对齐、面部数据增强、面部归一化4个步骤预处理面部图像;其次,基于多尺度特征融合算法提取表情特征,生成情感识别数据标签;最后,利用FasterR-CNN构建人脸面部情感识别模型,并识别人脸面部情感。实验结果表明,基于FasterR-CNN的人脸面部情感识别方法在6种基本表情中均具有90%以上的识别准确率。

基于Faster R-CNN的海底管道智能检测方法

为提高海底管道缺陷及组件的检测精度并实现智能化海底管道安全检测,提出一种基于快速区域卷积神经网络(Faster R-CNN)的海底管道智能检测方法。首先,通过基值校正和分段映射-伪彩色化方法,将漏磁检测信号转化为伪彩色图,以增强漏磁信号的关键特征;其次,基于多模态数据增强来提升检测模型的泛化能力;然后,基于多模态数据增强后的样本训练改进的Faster R-CNN网络,建立最优的智能检测模型;最后,以试验场和渤海在役管道为例,验证所提方法的有效性。结果表明:所提方法的平均检测精度可达93.8%,相较原始的Faster R-CNN算法提高8%,且平均交并比达到0.75,能够精准地实现海底油气管道多目标检测,保障海底管道的安全运行。

基于改进Faster RCNN的PCB表面缺陷检测研究

印刷电路板(PCB)在制造过程中不可避免地存在焊点缺焊、短路、毛刺、缺口、开路、余铜等微小缺陷。传统的基于机器视觉检测的缺陷检测方法存在检测速度慢、误检率和漏检率高、抗干扰能力弱等问题。为解决上述问题,提出一种基于改进快速区域卷积神经网络(Faster RCNN)的PCB表面缺陷检测方法。首先,在传统Faster RCNN框架的基础上,融入扩展特征金字塔网络(EFPN)以实现特征提取与融合,并进行多尺度检测,从而尽可能保留图像细节信息以提高检测性能。其次,利用K-means算法结合交并比(IoU)优化区域建议网络(RPN)结构中的锚框参数,使得生成的锚框方案更有针对性。试验结果表明,改进Faster RCNN在PCB缺陷数据集上的全类平均正确率(mAP)值达到93.4%、检测速度达到每秒21.79帧。所提方法可推广应用至芯片、光学器件表面微小缺陷在线检测,从而提升工业生产效率。

基于Transformer改进的Faster RCNN在复杂环境下的车辆检测

在监控视角中目标车辆较小、遮挡较为严重,导致检测精度低。通过探讨卷积神经网络和Transformer模型的互相借鉴和联系,并结合损失函数等常规改进,提出了新的Faster RCNN模型。通过借鉴Transformer模型的思想,对原有的特征提取网络进行了改进,将原block比例3∶4∶6∶3改为3∶3∶27∶3、卷积核由3×3改为7×7,增大其感受野,能够更好捕捉图像中的全局特征,使用DW卷积来减少参数量并略微提高性能,使用Channel shuffle解决通道间信息不交流的问题。将原先交并比IoU改为CIoU,与改进后的特征提取网络结合,进一步提高小目标和遮挡目标的检测效果。在UA-DETRAC数据集上,改进后的模型在mAP@0.5:0.95方面比原算法提高了20.20%,并在大、中、小目标下分别提高了15.8%、23%和45.8%,相较于其他模型,如YO⁃LOv7、YOLOv5和Cascade RCNN,mAP@0.5:0.95分别提高了3.3%、5%和6.69%。

基于改进的Faster RCNN的仪表自动识别方法

在环境复杂的工业场景中,仪表盘存在类别多、相似性高等问题,导致检测的识别效果较差、准确率不高。针对这一问题,提出了一种基于改进的更快速的区域卷积神经网络(Faster RCNN)的仪表自动识别方法。首先,采用残差网络(Resnet)101代替视觉几何群网络(VGG)16,进行了网络结构简化;然后,引入了特征金字塔网络(FPN),并将其改进为递归特征金字塔网络后进行了迭代融合,输出了特征图;接着,引入了注意力机制模块,根据特征的重要程度,完成了对输出通道权值的重新分配,增强了Faster RCNN对目标的运算能力;提出了改进非极大值抑制算法(Softer-NMS),通过降低置信度来确定准确的目标候选框;最后,采用Mosaic数据增强技术对可视对象类(VOC)2007数据集进行了扩充,对改进后的Faster RCNN模型进行了仪表自动识别的实验。研究结果表明:在相同工业环境下,与传统的Faster RCNN算法模型相比,改进后的Faster RCNN模型准确率为93.5%,较原模型提高了3.8%,mAP值为92.6%,较原模型提高了3.7%,可见该方法在实际生产中具有较强的鲁棒性与泛化能力,可满足工业上对智能检测的要求。

基于改进Faster R-CNN的铝型材表面缺陷检测研究

针对传统目标检测算法在铝型材表面缺陷检测中检测精度低、微小缺陷难以识别等问题,提出一种改进的Faster R-CNN算法。该算法对特征提取网络、感兴趣区域池化和锚框尺寸进行了优化,并在此基础上引入了特征金字塔和可变形卷积,以提升检测精度。实验表明,改进算法的平均精确度均值可达到86.73%,相较于原算法提升了8.70%,其对微小缺陷识别效果好,满足了工业上对缺陷检测的需求。

基于Faster R-CNN的田间西兰花幼苗图像检测方法

为解决自然环境下作物识别率不高、鲁棒性不强等问题,以西兰花幼苗为研究对象,提出了一种基于Faster R-CNN模型的作物检测方法。根据田间环境特点,采集不同光照强度、不同地面含水率和不同杂草密度下的西兰花幼苗图像,以确保样本多样性,并通过数据增强手段扩大样本量,制作PASCAL VOC格式数据集。针对此数据集训练Faster R-CNN模型,通过设计ResNet101、ResNet50与VGG16网络的对比试验,确定ResNet101网络为最优特征提取网络,其平均精度为90.89%,平均检测时间249 ms。在此基础上优化网络超参数,确定Dropout值为0.6时,模型识别效果最佳,其平均精度达到91.73%。结果表明,本文方法能够对自然环境下的西兰花幼苗进行有效检测,可为农业智能除草作业中的作物识别提供借鉴。

Diagnosis of primary clear cell carcinoma of the liver based on Faster region-based convolutional neural network

Background:Distinguishing between primary clear cell carcinoma of the liver(PCCCL)and common hepatocellular carcinoma(CHCC)through traditional inspection methods before the operation is difficult.This study aimed to establish a Faster region-based convolutional neural network(RCNN)model for the accurate differential diagnosis of PCCCL and CHCC.Methods:In this study,we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020.A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The Faster RCNN was used for deep learning of patients’data in the training validation set,and established a convolutional neural network model to distinguish PCCCL and CHCC.The accuracy,average precision,and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the Faster RCNN algorithm.Results:A total of 4392 images of 121 patients(1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC)were uesd in test set for deep learning and establishing the model,and 1072 images of 30 patients(320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC)were used to test the model.The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962(95%confidence interval[CI]:0.931-0.992).The average precision of the model for diagnosing PCCCL was 0.908(95%CI:0.823-0.993)and that for diagnosing CHCC was 0.907(95%CI:0.823-0.993).The recall of the model for diagnosing PCCCL was 0.951(95%CI:0.916-0.985)and that for diagnosing CHCC was 0.960(95%CI:0.854-0.962).The time to make a diagnosis using the model took an average of 4 s for each patient.Conclusion:The Faster RCNN model can accurately distinguish PCCCL and CHCC.This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.

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.

改进Faster R-CNN的田间苦瓜叶部病害检测

为实现在自然环境条件下对苦瓜叶部病害的目标检测,该研究提出了一种基于改进的更快速区域卷积神经网络(Faster Region with Convolutional Neural Network Features,Faster R-CNN)的苦瓜叶部病害目标检测方法。Faster R-CNN以残差结构卷积神经网络ResNet-50作为该次试验的特征提取网络,将其所得特征图输入到区域建议网络提取区域建议框,并且结合苦瓜叶部病害尺寸小的特点,对原始的Faster R-CNN进行修改,增加区域建议框的尺寸个数,并在ResNet-50的基础下融入了特征金字塔网络(Feature Pyramid Networks,FPN)。结果表明,该方法训练所得的深度学习网络模型具有良好的鲁棒性,平均精度均值(Mean Average Precision,MAP)值为78.85%;融入特征金字塔网络后,所得模型的平均精度均值为86.39%,提高了7.54%,苦瓜健康叶片、白粉病、灰斑病、蔓枯病、斑点病的平均精确率(Average Precision,AP)分别为89.24%、81.48%、83.31%、88.62%和89.28%,在灰斑病检测精度上比之前可提高了16.56%,每幅图像的检测时间达0.322 s,保证检测的实时性。该方法对复杂的自然环境下的苦瓜叶部病害检测具有较好的鲁棒性和较高的精度,对瓜果类疾病预防有重要的研究意义。

基于Faster R-CNN的航拍图像中绝缘子识别

为了解决传统绝缘子识别方法存在适用性不强、识别效率低的问题,结合深度卷积神经网络思想,提出一种从电网巡检航拍图像中自动识别绝缘子的方法。应用Faster R-CNN框架,结合电网巡检航拍图像数据库,构建绝缘子识别系统,自动识别航拍图像中的绝缘子,并分析不同模型和参数对识别精确度的影响。实验结果表明,相比于传统航拍绝缘子识别方法,采用深度卷积神经网络对航拍绝缘子进行学习和识别,具有较高的识别准确率和效率,可以很好地识别各种类型的绝缘子,识别性能大幅度提高。

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Faster R-CNN的新疆棉花幼苗与杂草识别方法

针对新疆棉田杂草的伴生特点带来的特征过拟合、精确率低等问题,以新疆棉花幼苗与杂草为研究对象,分析杂草识别率低的影响因素,建立了基于Faster R-CNN的网络识别模型.采集不同角度、不同自然环境和不同密集程度混合生长的棉花幼苗与杂草图像5370张.为确保样本质量以及多样性,利用颜色迁移和数据增强来提高图像的颜色特征与扩大样本量,以PASCAL VOC格式数据集进行网络模型训练.通过综合对比VGG16,VGG19,ResNet50和ResNet101这4种网络的识别时间与精度,选择VGG16网络训练Faster R-CNN模型.在此基础上设计了纵横比为1∶1的最佳锚尺度,在该模型下对新疆棉花幼苗与杂草进行识别,实现91.49%的平均识别精度,平均识别时间262 ms.研究结果为农业智能精确除草装备的研发提供了参考.

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。