To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks,a lightweight ResNet(LW-ResNet)model for apple disease rec...To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks,a lightweight ResNet(LW-ResNet)model for apple disease recognition is proposed.Based on the deep residual network(ResNet18),the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features.By improving the identity mapping structure to reduce information loss.By introducing the efficient channel attention module(ECANet)to suppress noise from a complex background.The experimental results show that the average precision,recall and F1-score of the LW-ResNet on the test set are 97.80%,97.92%and 97.85%,respectively.The parameter memory is 2.32 MB,which is 94%less than that of ResNet18.Compared with the classic lightweight networks SqueezeNet and MobileNetV2,LW-ResNet has obvious advantages in recognition performance,speed,parameter memory requirement and time complexity.The proposed model has the advantages of low computational cost,low storage cost,strong real-time performance,high identification accuracy,and strong practicability,which can meet the needs of real-time identification task of apple leaf disease on resource-constrained devices.展开更多
针对乒乓球目标检测方法易受环境、光线、速度等多种因素干扰导致精度和实时性不佳的问题,提出了一种基于YOLOv5s框架的轻量化乒乓球目标检测算法——SYOLO5(Shuffle-YOLOv5s)。首先,采用改进的ShuffleNetV2网络单元组合重构YOLOv5s主...针对乒乓球目标检测方法易受环境、光线、速度等多种因素干扰导致精度和实时性不佳的问题,提出了一种基于YOLOv5s框架的轻量化乒乓球目标检测算法——SYOLO5(Shuffle-YOLOv5s)。首先,采用改进的ShuffleNetV2网络单元组合重构YOLOv5s主干网络,提高特征提取速度;其次,在特征融合的过程中引入高效通道注意力(ECA)机制,有效提升模型的检测性能;接着,采用SIoU Loss(S-Intersection over Union)作为定位损失函数提升网络的收敛速度和定位精度;最后,贴合乒乓球小尺寸的特点,采用双尺度目标检测,进一步提高模型推理速度。实验结果表明,所提算法与YOLOv5s相比,参数量和计算量分别减少了80%和60%,精确率提升了1.9个百分点。展开更多
随着食用菌行业由自动化向智能化、信息化发展的趋势越来越明显,为了实现现代化菇房中平菇的准确检测,解决工厂化平菇栽培中收获阶段平菇之间相互遮挡等问题,帮助平菇采收机器人进行准确的自动化采收,该研究提出了一种基于YOLOv5(you on...随着食用菌行业由自动化向智能化、信息化发展的趋势越来越明显,为了实现现代化菇房中平菇的准确检测,解决工厂化平菇栽培中收获阶段平菇之间相互遮挡等问题,帮助平菇采收机器人进行准确的自动化采收,该研究提出了一种基于YOLOv5(you only look once version 5)模型的OMM-YOLO(ostreatus measure modle-YOLO)平菇目标检测与分类模型。通过在YOLOv5模型的Backbone层添加注意力模块,对输入的平菇图像特征进行动态加权,以获得更详细的特征信息,并在Neck层采用加权双向特征金字塔网络,通过与不同的特征层融合,提高算法的平菇目标检测的精度。此外,为了改善算法的准确性和边界框纵横比的收敛速度,该文采用了EIoU(enhanced intersection over union)损失函数替代了原有的损失函数。试验结果表明,与原始模型相比,改进模型OMM-YOLO对成熟平菇、未成熟平菇和未生长平菇的平均精度均值分别提高了0.4个百分点、4.5个百分点和1.1个百分点。与当前主流模型Resnet50、VGG16、YOLOv3、YOLOv4、YOLOv5m和YOLOv7相比,该模型的精确率、召回率和检测精度均处于优势,适用于收集现代化菇房中的平菇信息,有效避免了平菇之间因相互遮挡而产生的误检测现象。菇房平菇目标检测可以自动化地检测平菇的数量、生长状态等信息,帮助菇房工作人员掌握菇房内的菇况,及时调整温湿度等环境条件,提高生产效率,并且对可以对平菇进行质量控制,确保平菇产品的统一性和品质稳定性。同时可以减少对人工的依赖,降低人力成本,实现可持续发展,对智能化现代菇房建设具有积极作用。展开更多
基金funded by the Science and Technology Development Program of Jilin Province(20190301024NY)the Precision Agriculture and Big Data Engineering Research Center of Jilin Province(2020C005).
文摘To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks,a lightweight ResNet(LW-ResNet)model for apple disease recognition is proposed.Based on the deep residual network(ResNet18),the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features.By improving the identity mapping structure to reduce information loss.By introducing the efficient channel attention module(ECANet)to suppress noise from a complex background.The experimental results show that the average precision,recall and F1-score of the LW-ResNet on the test set are 97.80%,97.92%and 97.85%,respectively.The parameter memory is 2.32 MB,which is 94%less than that of ResNet18.Compared with the classic lightweight networks SqueezeNet and MobileNetV2,LW-ResNet has obvious advantages in recognition performance,speed,parameter memory requirement and time complexity.The proposed model has the advantages of low computational cost,low storage cost,strong real-time performance,high identification accuracy,and strong practicability,which can meet the needs of real-time identification task of apple leaf disease on resource-constrained devices.
文摘针对乒乓球目标检测方法易受环境、光线、速度等多种因素干扰导致精度和实时性不佳的问题,提出了一种基于YOLOv5s框架的轻量化乒乓球目标检测算法——SYOLO5(Shuffle-YOLOv5s)。首先,采用改进的ShuffleNetV2网络单元组合重构YOLOv5s主干网络,提高特征提取速度;其次,在特征融合的过程中引入高效通道注意力(ECA)机制,有效提升模型的检测性能;接着,采用SIoU Loss(S-Intersection over Union)作为定位损失函数提升网络的收敛速度和定位精度;最后,贴合乒乓球小尺寸的特点,采用双尺度目标检测,进一步提高模型推理速度。实验结果表明,所提算法与YOLOv5s相比,参数量和计算量分别减少了80%和60%,精确率提升了1.9个百分点。
文摘随着食用菌行业由自动化向智能化、信息化发展的趋势越来越明显,为了实现现代化菇房中平菇的准确检测,解决工厂化平菇栽培中收获阶段平菇之间相互遮挡等问题,帮助平菇采收机器人进行准确的自动化采收,该研究提出了一种基于YOLOv5(you only look once version 5)模型的OMM-YOLO(ostreatus measure modle-YOLO)平菇目标检测与分类模型。通过在YOLOv5模型的Backbone层添加注意力模块,对输入的平菇图像特征进行动态加权,以获得更详细的特征信息,并在Neck层采用加权双向特征金字塔网络,通过与不同的特征层融合,提高算法的平菇目标检测的精度。此外,为了改善算法的准确性和边界框纵横比的收敛速度,该文采用了EIoU(enhanced intersection over union)损失函数替代了原有的损失函数。试验结果表明,与原始模型相比,改进模型OMM-YOLO对成熟平菇、未成熟平菇和未生长平菇的平均精度均值分别提高了0.4个百分点、4.5个百分点和1.1个百分点。与当前主流模型Resnet50、VGG16、YOLOv3、YOLOv4、YOLOv5m和YOLOv7相比,该模型的精确率、召回率和检测精度均处于优势,适用于收集现代化菇房中的平菇信息,有效避免了平菇之间因相互遮挡而产生的误检测现象。菇房平菇目标检测可以自动化地检测平菇的数量、生长状态等信息,帮助菇房工作人员掌握菇房内的菇况,及时调整温湿度等环境条件,提高生产效率,并且对可以对平菇进行质量控制,确保平菇产品的统一性和品质稳定性。同时可以减少对人工的依赖,降低人力成本,实现可持续发展,对智能化现代菇房建设具有积极作用。
