The detection of Ochotona Curzoniae serves as a fundamental component for estimating the population size of this species and for analyzing the dynamics of its population fluctuations.In natural environments,the pixels...The detection of Ochotona Curzoniae serves as a fundamental component for estimating the population size of this species and for analyzing the dynamics of its population fluctuations.In natural environments,the pixels representing Ochotona Curzoniae constitute a small fraction of the total pixels,and their distinguishing features are often subtle,complicating the target detection process.To effectively extract the characteristics of these small targets,a feature fusion approach that utilizes up-sampling and channel integration from various layers within a CNN can significantly enhance the representation of target features,ultimately improving detection accuracy.However,the top-down fusion of features from different layers may lead to information duplication and semantic bias,resulting in redundancy and high-frequency noise.To address the challenges of information redundancy and high-frequency noise during the feature fusion process in CNN,we have developed a target detection model for Ochotona Curzoniae.This model is based on a spatial-channel reconfiguration convolutional(SCConv)attentional mechanism and feature fusion(FFBCA),integrated with the Faster R-CNN framework.It consists of a feature extraction network,an attention mechanism-based feature fusion module,and a jump residual connection fusion module.Initially,we designed a dual attention mechanism feature fusion module that employs spatial-channel reconstruction convolution.In the spatial dimension,the attention mechanism adopts a separation-reconstruction approach,calculating a weight matrix for the spatial information within the feature map through group normalization.This process directs the model to concentrate on feature information assigned varying weights,thereby reducing redundancy during feature fusion.In the channel dimension,the attention mechanism utilizes a partition-transpose-fusion method,segmenting the input feature map into high-noise and low-noise components based on the variance of the feature information.The high-noise segment is processed through a low-pass filter constructed from pointwise convolution(PWC)to eliminate some high-frequency noise,while the low-noise segment employs a bottleneck structure with global average pooling(GAP)to generate a weight matrix that emphasizes the significance of channel dimension feature information.This approach diminishes the model’s focus on low-weight feature information,thereby preserving low-frequency semantic information while reducing information redundancy.Furthermore,we have developed a novel feature extraction network,ResNeXt-S,by integrating the Sim attention mechanism into ResNeXt50.This configuration assigns three-dimensional attention weights to each position within the feature map,thereby enhancing the local feature information of small targets while reducing background noise.Finally,we constructed a jump residual connection fusion module to minimize the loss of high-level semantic information during the feature fusion process.Experiments on Ochotona Curzoniae target detection on the Ochotona Curzoniae dataset show that the detection accuracy of the model in this paper is 92.3%,which is higher than that of FSSD512(84.6%),TDFSSD512(81.3%),FPN(86.5%),FFBAM(88.5%),Faster R-CNN(89.6%),and SSD512(88.6%)detection accuracies.展开更多
[Objective]The accurate identification of maize tassels is critical for the production of hybrid seed.Existing object detection models in complex farmland scenarios face limitations such as restricted data diversity,i...[Objective]The accurate identification of maize tassels is critical for the production of hybrid seed.Existing object detection models in complex farmland scenarios face limitations such as restricted data diversity,insufficient feature extraction,high computational load,and low detection efficiency.To address these challenges,a real-time field maize tassel detection model,LightTassel-YOLO(You Only Look Once)based on an improved YOLOv11n is proposed.The model is designed to quickly and accurately identify maize tassels,enabling efficient operation of detasseling unmanned aerial vehicles(UAVs)and reducing the impact of manual intervention.[Methods]Data was continuously collected during the tasseling stage of maize from 2023 to 2024 using UAVs,establishing a large-scale,high-quality maize tassel dataset that covered different maize tasseling stages,multiple varieties,varying altitudes,and diverse meteorological conditions.First,EfficientViT(Efficient vision transformer)was applied as the backbone network to enhance the ability to perceive information across multi-scale features.Second,the C2PSA-CPCA(Convolutional block with parallel spatial attention with channel prior convolutional attention)module was designed to dynamically assign attention weights to the channel and spatial dimensions of feature maps,effectively enhancing the network's capability to extract target features while reducing computational complexity.Finally,the C3k2-SCConv module was constructed to facilitate representative feature learning and achieve low-cost spatial feature reconstruction,thereby improving the model's detection accuracy.[Results and Discussions]The results demonstrated that LightTassel-YOLO provided a reliable method for maize tassel detection.The final model achieved an accuracy of 92.6%,a recall of 89.1%,and an AP@0.5 of 94.7%,representing improvements of 2.5,3.8 and 4.0 percentage points over the baseline model YOLOv11n,respectively.The model had only 3.23 M parameters and a computational cost of 6.7 GFLOPs.In addition,LightTassel-YOLO was compared with mainstream object detection algorithms such as Faster R-CNN,SSD,and multiple versions of the YOLO series.The results demonstrated that the proposed method outperformed these algorithms in overall performance and exhibits excellent adaptability in typical field scenarios.[Conclusions]The proposed method provides an effective theoretical framework for precise maize tassel monitoring and holds significant potential for advancing intelligent field management practices.展开更多
针对传统的含水率检测方法具有破坏性、检测时间长的问题,提出了基于空间通道和组混洗你只看一次版本8纳米型(spatial channel and group shuffle-you only look once version 8 nano,SG-YOLOv8n)算法的茶叶萎凋过程含水率检测方法。首...针对传统的含水率检测方法具有破坏性、检测时间长的问题,提出了基于空间通道和组混洗你只看一次版本8纳米型(spatial channel and group shuffle-you only look once version 8 nano,SG-YOLOv8n)算法的茶叶萎凋过程含水率检测方法。首先,构建萎凋过程中不同含水率的茶叶图像数据集;然后,为提高算法的感知能力,该算法中引入了卷积块注意力模块(convolutional block attention module,CBAM),在不增加网络复杂性的情况下改善了性能;最后,为了提升平均精确率均值和浮点运算速度,使用组混洗卷积(group shuffle convolution,GSConv)代替颈部网络的标准卷积,并使用卷积到全连接的空间和通道重建卷积(convolution to fully connected-spatial and channel reconstruction convolution,C2f-SCConv)模块代替主干网络的卷积到全连接(convolution to fully connected,C2f)模块。结果表明,SG-YOLOv8n算法相比于原版算法的平均精确率均值、精确率分别提升了4.6%、5.7%,检测速度达到了156.0帧/秒。该算法能提升茶鲜叶萎凋过程中含水率的检测精确率,还能实现实时检测,能满足边缘计算设备的要求。展开更多
基金funded by the National Natural Science Foundation of China(Grant Nos.62161019,62061024).
文摘The detection of Ochotona Curzoniae serves as a fundamental component for estimating the population size of this species and for analyzing the dynamics of its population fluctuations.In natural environments,the pixels representing Ochotona Curzoniae constitute a small fraction of the total pixels,and their distinguishing features are often subtle,complicating the target detection process.To effectively extract the characteristics of these small targets,a feature fusion approach that utilizes up-sampling and channel integration from various layers within a CNN can significantly enhance the representation of target features,ultimately improving detection accuracy.However,the top-down fusion of features from different layers may lead to information duplication and semantic bias,resulting in redundancy and high-frequency noise.To address the challenges of information redundancy and high-frequency noise during the feature fusion process in CNN,we have developed a target detection model for Ochotona Curzoniae.This model is based on a spatial-channel reconfiguration convolutional(SCConv)attentional mechanism and feature fusion(FFBCA),integrated with the Faster R-CNN framework.It consists of a feature extraction network,an attention mechanism-based feature fusion module,and a jump residual connection fusion module.Initially,we designed a dual attention mechanism feature fusion module that employs spatial-channel reconstruction convolution.In the spatial dimension,the attention mechanism adopts a separation-reconstruction approach,calculating a weight matrix for the spatial information within the feature map through group normalization.This process directs the model to concentrate on feature information assigned varying weights,thereby reducing redundancy during feature fusion.In the channel dimension,the attention mechanism utilizes a partition-transpose-fusion method,segmenting the input feature map into high-noise and low-noise components based on the variance of the feature information.The high-noise segment is processed through a low-pass filter constructed from pointwise convolution(PWC)to eliminate some high-frequency noise,while the low-noise segment employs a bottleneck structure with global average pooling(GAP)to generate a weight matrix that emphasizes the significance of channel dimension feature information.This approach diminishes the model’s focus on low-weight feature information,thereby preserving low-frequency semantic information while reducing information redundancy.Furthermore,we have developed a novel feature extraction network,ResNeXt-S,by integrating the Sim attention mechanism into ResNeXt50.This configuration assigns three-dimensional attention weights to each position within the feature map,thereby enhancing the local feature information of small targets while reducing background noise.Finally,we constructed a jump residual connection fusion module to minimize the loss of high-level semantic information during the feature fusion process.Experiments on Ochotona Curzoniae target detection on the Ochotona Curzoniae dataset show that the detection accuracy of the model in this paper is 92.3%,which is higher than that of FSSD512(84.6%),TDFSSD512(81.3%),FPN(86.5%),FFBAM(88.5%),Faster R-CNN(89.6%),and SSD512(88.6%)detection accuracies.
文摘[Objective]The accurate identification of maize tassels is critical for the production of hybrid seed.Existing object detection models in complex farmland scenarios face limitations such as restricted data diversity,insufficient feature extraction,high computational load,and low detection efficiency.To address these challenges,a real-time field maize tassel detection model,LightTassel-YOLO(You Only Look Once)based on an improved YOLOv11n is proposed.The model is designed to quickly and accurately identify maize tassels,enabling efficient operation of detasseling unmanned aerial vehicles(UAVs)and reducing the impact of manual intervention.[Methods]Data was continuously collected during the tasseling stage of maize from 2023 to 2024 using UAVs,establishing a large-scale,high-quality maize tassel dataset that covered different maize tasseling stages,multiple varieties,varying altitudes,and diverse meteorological conditions.First,EfficientViT(Efficient vision transformer)was applied as the backbone network to enhance the ability to perceive information across multi-scale features.Second,the C2PSA-CPCA(Convolutional block with parallel spatial attention with channel prior convolutional attention)module was designed to dynamically assign attention weights to the channel and spatial dimensions of feature maps,effectively enhancing the network's capability to extract target features while reducing computational complexity.Finally,the C3k2-SCConv module was constructed to facilitate representative feature learning and achieve low-cost spatial feature reconstruction,thereby improving the model's detection accuracy.[Results and Discussions]The results demonstrated that LightTassel-YOLO provided a reliable method for maize tassel detection.The final model achieved an accuracy of 92.6%,a recall of 89.1%,and an AP@0.5 of 94.7%,representing improvements of 2.5,3.8 and 4.0 percentage points over the baseline model YOLOv11n,respectively.The model had only 3.23 M parameters and a computational cost of 6.7 GFLOPs.In addition,LightTassel-YOLO was compared with mainstream object detection algorithms such as Faster R-CNN,SSD,and multiple versions of the YOLO series.The results demonstrated that the proposed method outperformed these algorithms in overall performance and exhibits excellent adaptability in typical field scenarios.[Conclusions]The proposed method provides an effective theoretical framework for precise maize tassel monitoring and holds significant potential for advancing intelligent field management practices.
文摘针对当前配电网涉电公共安全隐患识别过程中存在的时效性不足与准确性偏低的问题,提出一种基于YOLOv8的改进目标检测模型。该模型通过以下三方面创新有效提升了检测性能:首先,在骨干网络中嵌入BoT3(bottom-up transformer 3)模块,基于“系统化全局感知”思想构建目标与背景之间的长程依赖关系,增强全局上下文特征提取能力,重点提升对小尺寸隐患的识别精度;其次,在颈部网络引入C2f(channel to feature)与空间与通道重建卷积(spatial and channel reconstruction convolution,SCConv)模块,在减少参数和计算量的同时实现网络轻量化,并提高多尺度特征融合效率,从而提高检测实时性;最后,在检测头中嵌入坐标注意力(coordinate attention,CA),加强对小目标和模糊目标的特征捕捉能力。实验表明,改进模型显著提升了涉电安全隐患的识别性能,mAP@0.5和mAP@0.5∶0.95分别达到0.974和0.710,后者较基线提升4.6%,验证了其在识别效率与准确率上的优势,为配电网安全隐患的智能识别提供了更高效的解决方案。
文摘针对传统的含水率检测方法具有破坏性、检测时间长的问题,提出了基于空间通道和组混洗你只看一次版本8纳米型(spatial channel and group shuffle-you only look once version 8 nano,SG-YOLOv8n)算法的茶叶萎凋过程含水率检测方法。首先,构建萎凋过程中不同含水率的茶叶图像数据集;然后,为提高算法的感知能力,该算法中引入了卷积块注意力模块(convolutional block attention module,CBAM),在不增加网络复杂性的情况下改善了性能;最后,为了提升平均精确率均值和浮点运算速度,使用组混洗卷积(group shuffle convolution,GSConv)代替颈部网络的标准卷积,并使用卷积到全连接的空间和通道重建卷积(convolution to fully connected-spatial and channel reconstruction convolution,C2f-SCConv)模块代替主干网络的卷积到全连接(convolution to fully connected,C2f)模块。结果表明,SG-YOLOv8n算法相比于原版算法的平均精确率均值、精确率分别提升了4.6%、5.7%,检测速度达到了156.0帧/秒。该算法能提升茶鲜叶萎凋过程中含水率的检测精确率,还能实现实时检测,能满足边缘计算设备的要求。
