针对目标识别与抓取领域中CNN、Faster-RCNN等传统神经网络系列算法的识别准确率不高,实时性较差的问题,提出一种基于YOLOv3的改进神经网络算法。改进的YOLOv3算法主要是引用Inception网络思想,通过不同尺度的卷积核对目标进行多尺度特...针对目标识别与抓取领域中CNN、Faster-RCNN等传统神经网络系列算法的识别准确率不高,实时性较差的问题,提出一种基于YOLOv3的改进神经网络算法。改进的YOLOv3算法主要是引用Inception网络思想,通过不同尺度的卷积核对目标进行多尺度特征提取,在增加网络宽度的同时减少YOLOv3网络的循环次数。同时,YOLOv3算法对于anchor box的选取方式使用Meanshift(均值漂移)聚类算法与K-means聚类算法相结合的方式进行改进,解决了K值需要人为测定的问题。改进的YOLOv3算法在自制数据集进行对比实验,实验结果表明:改进YOLOv3算法的mAP(Mean Average Precision)值要高于YOLOv3算法10%,在识别速度上提高了9%,在充分满足实时识别的同时提高了对中小目标识别的准确率。展开更多
针对水稻图像中复杂背景带来的病斑难以识别、检测速度慢等问题,以水稻稻瘟病、白叶枯病和胡麻斑病图像为研究对象,提出一种基于改进YOLOv4的水稻病害检测方法,该方法以YOLOv4模型为主体框架,采用轻量级网络MobileNet V3代替原始主干网...针对水稻图像中复杂背景带来的病斑难以识别、检测速度慢等问题,以水稻稻瘟病、白叶枯病和胡麻斑病图像为研究对象,提出一种基于改进YOLOv4的水稻病害检测方法,该方法以YOLOv4模型为主体框架,采用轻量级网络MobileNet V3代替原始主干网络CSPDarkNet-53,并通过在颈部网络添加坐标注意力模块(coordinate attention module,CAM)来提高模型的性能。结果表明,改进后的模型对水稻稻瘟病、白叶枯病、胡麻斑病的识别准确率均有所提升,平均精度均值(mean average precision,mAP)为85.34%,与原始YOLOv4模型相比,mAP提高了1.32%,每秒钟检测图像的帧数(frames per second,FPS)为53.43帧/s,检测速度提高了49.62%,说明研究得出的方法具有较高的平均准确率及较快的检测速度,能够用于田间复杂环境下的水稻病害快速识别。展开更多
针对部署于有限算力平台的YOLOv3(you only look once v3)算法对电容器外观缺陷存在检测速度较慢的问题,提出了基于YOLOv3算法改进的轻量化算法MQYOLOv3。首先采用轻量化网络MobileNet v2作为特征提取模块,通过利用深度可分离式卷积替...针对部署于有限算力平台的YOLOv3(you only look once v3)算法对电容器外观缺陷存在检测速度较慢的问题,提出了基于YOLOv3算法改进的轻量化算法MQYOLOv3。首先采用轻量化网络MobileNet v2作为特征提取模块,通过利用深度可分离式卷积替换一般卷积操作,使得模型的参数量大幅度降低进而提高模型的检测速度,同时也带来了检测精度的降低;然后在网络结构中嵌入空间金字塔池化结构实现局部特征与全局特征的融合、引入距离交并比(distance intersection over union,DIoU)损失函数优化交并比(intersection over union,IoU)损失函数以及使用Mish激活函数优化Leaky ReLU激活函数来提高模型的检测精度。本文采用自制的电容器外观缺陷数据集进行实验,轻量化MQYOLOv3算法的平均精度均值(mean average precision,mAP)为87.96%,较优化前降低了1.16%,检测速度从1.5 FPS提升到7.7 FPS。实验表明,本文设计的轻量化MQYOLOv3算法在保证检测精度的同时,提高了检测速度。展开更多
Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in dee...Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in deep learning for fire detection.Firstly,the depthwise separable convolution is used to classify fire images,which saves a lot of detection time under the premise of ensuring detection accuracy.Secondly,You Only Look Once version 3(YOLOv3)target regression function is used to output the fire position information for the images whose classification result is fire,which avoids the problem that the accuracy of detection cannot be guaranteed by using YOLOv3 for target classification and position regression.At the same time,the detection time of target regression for images without fire is greatly reduced saved.The experiments were tested using a network public database.The detection accuracy reached 98%and the detection rate reached 38fps.This method not only saves the workload of manually extracting flame characteristics,reduces the calculation cost,and reduces the amount of parameters,but also improves the detection accuracy and detection rate.展开更多
Deep Learning has been utilized in computer vision for object detection for almost a decade.Real-time object detection for robotic inspection and harvesting has gained interest during this time as a possible technique...Deep Learning has been utilized in computer vision for object detection for almost a decade.Real-time object detection for robotic inspection and harvesting has gained interest during this time as a possible technique for highqualitymachine assistance during agriculture applications.We utilize RGB and thermal images of chili peppers in an environment of various amounts of debris,pepper overlapping,and ambient lighting,train this dataset,and compare object detection methods.Results are presented from the real-time and less than real-time object detection models.Two advanced deep learning algorithms,Mask-Regional Convolutional Neural Networks(Mask-RCNN)and You Only Look Once version 3(YOLOv3)are compared in terms of object detection accuracy and computational costs.When utilizing the YOLOv3 architecture,an overall training mean average precision(mAP)value of 1.0 is achieved.Most testing images from this model score within a range from 97 to 100%confidence levels in natural environment.It is shown that the YOLOv3 algorithm has superior capabilities to the Mask-RCNNwith over 10 times the computational speed on the chili dataset.However,some of the RGB test images resulted in lowclassification scoreswhen heavy debris is present in the image.A significant improvement in the real-time classification scores was observed when the thermal images were used,especially with heavy debris present.We found and report improved prediction scores with a thermal imagery dataset where YOLOv3 struggled on the RGB images.It was shown that mapping temperature differences between the pepper and plant/debris can provide significant features for object detection in real-time and can help improve accuracy of predictionswith heavy debris,variant ambient lighting,and overlapping of peppers.In addition,successful thermal imaging for real-time robotic harvesting could allow the harvesting period to become more efficient and open up harvesting opportunity in low light situations.展开更多
文摘针对目标识别与抓取领域中CNN、Faster-RCNN等传统神经网络系列算法的识别准确率不高,实时性较差的问题,提出一种基于YOLOv3的改进神经网络算法。改进的YOLOv3算法主要是引用Inception网络思想,通过不同尺度的卷积核对目标进行多尺度特征提取,在增加网络宽度的同时减少YOLOv3网络的循环次数。同时,YOLOv3算法对于anchor box的选取方式使用Meanshift(均值漂移)聚类算法与K-means聚类算法相结合的方式进行改进,解决了K值需要人为测定的问题。改进的YOLOv3算法在自制数据集进行对比实验,实验结果表明:改进YOLOv3算法的mAP(Mean Average Precision)值要高于YOLOv3算法10%,在识别速度上提高了9%,在充分满足实时识别的同时提高了对中小目标识别的准确率。
文摘针对水稻图像中复杂背景带来的病斑难以识别、检测速度慢等问题,以水稻稻瘟病、白叶枯病和胡麻斑病图像为研究对象,提出一种基于改进YOLOv4的水稻病害检测方法,该方法以YOLOv4模型为主体框架,采用轻量级网络MobileNet V3代替原始主干网络CSPDarkNet-53,并通过在颈部网络添加坐标注意力模块(coordinate attention module,CAM)来提高模型的性能。结果表明,改进后的模型对水稻稻瘟病、白叶枯病、胡麻斑病的识别准确率均有所提升,平均精度均值(mean average precision,mAP)为85.34%,与原始YOLOv4模型相比,mAP提高了1.32%,每秒钟检测图像的帧数(frames per second,FPS)为53.43帧/s,检测速度提高了49.62%,说明研究得出的方法具有较高的平均准确率及较快的检测速度,能够用于田间复杂环境下的水稻病害快速识别。
文摘针对部署于有限算力平台的YOLOv3(you only look once v3)算法对电容器外观缺陷存在检测速度较慢的问题,提出了基于YOLOv3算法改进的轻量化算法MQYOLOv3。首先采用轻量化网络MobileNet v2作为特征提取模块,通过利用深度可分离式卷积替换一般卷积操作,使得模型的参数量大幅度降低进而提高模型的检测速度,同时也带来了检测精度的降低;然后在网络结构中嵌入空间金字塔池化结构实现局部特征与全局特征的融合、引入距离交并比(distance intersection over union,DIoU)损失函数优化交并比(intersection over union,IoU)损失函数以及使用Mish激活函数优化Leaky ReLU激活函数来提高模型的检测精度。本文采用自制的电容器外观缺陷数据集进行实验,轻量化MQYOLOv3算法的平均精度均值(mean average precision,mAP)为87.96%,较优化前降低了1.16%,检测速度从1.5 FPS提升到7.7 FPS。实验表明,本文设计的轻量化MQYOLOv3算法在保证检测精度的同时,提高了检测速度。
基金This work was supported by Liaoning Provincial Science Public Welfare Research Fund Project(No.2016002006)Liaoning Provincial Department of Education Scientific Research Service Local Project(No.L201708).
文摘Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in deep learning for fire detection.Firstly,the depthwise separable convolution is used to classify fire images,which saves a lot of detection time under the premise of ensuring detection accuracy.Secondly,You Only Look Once version 3(YOLOv3)target regression function is used to output the fire position information for the images whose classification result is fire,which avoids the problem that the accuracy of detection cannot be guaranteed by using YOLOv3 for target classification and position regression.At the same time,the detection time of target regression for images without fire is greatly reduced saved.The experiments were tested using a network public database.The detection accuracy reached 98%and the detection rate reached 38fps.This method not only saves the workload of manually extracting flame characteristics,reduces the calculation cost,and reduces the amount of parameters,but also improves the detection accuracy and detection rate.
文摘Deep Learning has been utilized in computer vision for object detection for almost a decade.Real-time object detection for robotic inspection and harvesting has gained interest during this time as a possible technique for highqualitymachine assistance during agriculture applications.We utilize RGB and thermal images of chili peppers in an environment of various amounts of debris,pepper overlapping,and ambient lighting,train this dataset,and compare object detection methods.Results are presented from the real-time and less than real-time object detection models.Two advanced deep learning algorithms,Mask-Regional Convolutional Neural Networks(Mask-RCNN)and You Only Look Once version 3(YOLOv3)are compared in terms of object detection accuracy and computational costs.When utilizing the YOLOv3 architecture,an overall training mean average precision(mAP)value of 1.0 is achieved.Most testing images from this model score within a range from 97 to 100%confidence levels in natural environment.It is shown that the YOLOv3 algorithm has superior capabilities to the Mask-RCNNwith over 10 times the computational speed on the chili dataset.However,some of the RGB test images resulted in lowclassification scoreswhen heavy debris is present in the image.A significant improvement in the real-time classification scores was observed when the thermal images were used,especially with heavy debris present.We found and report improved prediction scores with a thermal imagery dataset where YOLOv3 struggled on the RGB images.It was shown that mapping temperature differences between the pepper and plant/debris can provide significant features for object detection in real-time and can help improve accuracy of predictionswith heavy debris,variant ambient lighting,and overlapping of peppers.In addition,successful thermal imaging for real-time robotic harvesting could allow the harvesting period to become more efficient and open up harvesting opportunity in low light situations.
