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 i...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.展开更多
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...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。展开更多
文摘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.
