PDF(Pubmed)
Abstract：
UNASSIGNED: The precise detection of weeds in the field is the premise of implementing weed management. However, the similar color, morphology, and occlusion between wheat and weeds pose a challenge to the detection of weeds. In this study, a CSCW-YOLOv7 based on an improved YOLOv7 architecture was proposed to identify five types of weeds in complex wheat fields.
UNASSIGNED: First, a dataset was constructed for five weeds that are commonly found, namely, Descurainia sophia, thistle, golden saxifrage, shepherd\'s purse herb, and Artemisia argyi. Second, a wheat weed detection model called CSCW-YOLOv7 was proposed to achieve the accurate identification and classification of wheat weeds. In the CSCW-YOLOv7, the CARAFE operator was introduced as an up-sampling algorithm to improve the recognition of small targets. Then, the Squeeze-and-Excitation (SE) network was added to the Extended Latent Attention Networks (ELAN) module in the backbone network and the concatenation layer in the feature fusion module to enhance important weed features and suppress irrelevant features. In addition, the contextual transformer (CoT) module, a transformer-based architectural design, was used to capture global information and enhance self-attention by mining contextual information between neighboring keys. Finally, the Wise Intersection over Union (WIoU) loss function introducing a dynamic nonmonotonic focusing mechanism was employed to better predict the bounding boxes of the occluded weed.
UNASSIGNED: The ablation experiment results showed that the CSCW-YOLOv7 achieved the best performance among the other models. The accuracy, recall, and mean average precision (mAP) values of the CSCW-YOLOv7 were 97.7%, 98%, and 94.4%, respectively. Compared with the baseline YOLOv7, the improved CSCW-YOLOv7 obtained precision, recall, and mAP increases of 1.8%, 1%, and 2.1%, respectively. Meanwhile, the parameters were compressed by 10.7% with a 3.8-MB reduction, resulting in a 10% decrease in floating-point operations per second (FLOPs). The Gradient-weighted Class Activation Mapping (Grad-CAM) visualization method suggested that the CSCW-YOLOv7 can learn a more representative set of features that can help better locate the weeds of different scales in complex field environments. In addition, the performance of the CSCW-YOLOv7 was compared to the widely used deep learning models, and results indicated that the CSCW-YOLOv7 exhibits a better ability to distinguish the overlapped weeds and small-scale weeds. The overall results suggest that the CSCW-YOLOv7 is a promising tool for the detection of weeds and has great potential for field applications.
UNASSIGNED: First, a dataset was constructed for five weeds that are commonly found, namely, Descurainia sophia, thistle, golden saxifrage, shepherd\'s purse herb, and Artemisia argyi. Second, a wheat weed detection model called CSCW-YOLOv7 was proposed to achieve the accurate identification and classification of wheat weeds. In the CSCW-YOLOv7, the CARAFE operator was introduced as an up-sampling algorithm to improve the recognition of small targets. Then, the Squeeze-and-Excitation (SE) network was added to the Extended Latent Attention Networks (ELAN) module in the backbone network and the concatenation layer in the feature fusion module to enhance important weed features and suppress irrelevant features. In addition, the contextual transformer (CoT) module, a transformer-based architectural design, was used to capture global information and enhance self-attention by mining contextual information between neighboring keys. Finally, the Wise Intersection over Union (WIoU) loss function introducing a dynamic nonmonotonic focusing mechanism was employed to better predict the bounding boxes of the occluded weed.
UNASSIGNED: The ablation experiment results showed that the CSCW-YOLOv7 achieved the best performance among the other models. The accuracy, recall, and mean average precision (mAP) values of the CSCW-YOLOv7 were 97.7%, 98%, and 94.4%, respectively. Compared with the baseline YOLOv7, the improved CSCW-YOLOv7 obtained precision, recall, and mAP increases of 1.8%, 1%, and 2.1%, respectively. Meanwhile, the parameters were compressed by 10.7% with a 3.8-MB reduction, resulting in a 10% decrease in floating-point operations per second (FLOPs). The Gradient-weighted Class Activation Mapping (Grad-CAM) visualization method suggested that the CSCW-YOLOv7 can learn a more representative set of features that can help better locate the weeds of different scales in complex field environments. In addition, the performance of the CSCW-YOLOv7 was compared to the widely used deep learning models, and results indicated that the CSCW-YOLOv7 exhibits a better ability to distinguish the overlapped weeds and small-scale weeds. The overall results suggest that the CSCW-YOLOv7 is a promising tool for the detection of weeds and has great potential for field applications.
摘要：
■田间杂草的精确检测是实施杂草管理的前提。然而,相似的颜色,形态学,小麦和杂草之间的闭塞对杂草的检测提出了挑战。在这项研究中,提出了基于改进的YOLOv7体系结构的CSCW-YOLOv7，以识别复杂麦田中的五种杂草。
■首先，为常见的五种杂草构建了一个数据集，即,DescurainiaSophia,蓟,金色的虎杖,牧羊人的钱包药草，和Artemisiaargyi。第二,提出了一种称为CSCW-YOLOv7的小麦杂草检测模型，以实现对小麦杂草的准确识别和分类。在CSCW-YOLOv7中，引入了CARAFE算子作为上采样算法，以提高对小目标的识别。然后，在骨干网络的扩展潜在注意网络(ELAN)模块和特征融合模块的级联层中添加了挤压激励(SE)网络,以增强重要的杂草特征并抑制无关特征。此外,上下文转换器(CoT)模块，基于变压器的建筑设计，用于捕获全局信息并通过挖掘相邻键之间的上下文信息来增强自我注意力。最后,引入动态非单调聚焦机制的WiseIntersectionoverUnion（WIoU）损失函数被用来更好地预测被遮挡杂草的边界框。
■消融实验结果表明，CSCW-YOLOv7在其他型号中取得了最佳性能。准确性,召回，CSCW-YOLOv7的平均精度（mAP）值为97.7%，98%,94.4%,分别。与基线YOLOv7相比，改进的CSCW-YOLOv7获得了精度，召回，mAP增加1.8%，1%,和2.1%，分别。同时,参数压缩了10.7%，减少了3.8MB，导致每秒浮点运算(FLOP)减少10%。梯度加权类激活图（Grad-CAM）可视化方法建议CSCW-YOLOv7可以学习一组更具代表性的特征，这些特征可以帮助在复杂的田间环境中更好地定位不同尺度的杂草。此外,将CSCW-YOLOv7的性能与广泛使用的深度学习模型进行了比较，结果表明，CSCW-YOLOv7具有更好的区分重叠杂草和小规模杂草的能力。总体结果表明，CSCW-YOLOv7是检测杂草的有前途的工具，具有很大的田间应用潜力。
■首先，为常见的五种杂草构建了一个数据集，即,DescurainiaSophia,蓟,金色的虎杖,牧羊人的钱包药草，和Artemisiaargyi。第二,提出了一种称为CSCW-YOLOv7的小麦杂草检测模型，以实现对小麦杂草的准确识别和分类。在CSCW-YOLOv7中，引入了CARAFE算子作为上采样算法，以提高对小目标的识别。然后，在骨干网络的扩展潜在注意网络(ELAN)模块和特征融合模块的级联层中添加了挤压激励(SE)网络,以增强重要的杂草特征并抑制无关特征。此外,上下文转换器(CoT)模块，基于变压器的建筑设计，用于捕获全局信息并通过挖掘相邻键之间的上下文信息来增强自我注意力。最后,引入动态非单调聚焦机制的WiseIntersectionoverUnion（WIoU）损失函数被用来更好地预测被遮挡杂草的边界框。
■消融实验结果表明，CSCW-YOLOv7在其他型号中取得了最佳性能。准确性,召回，CSCW-YOLOv7的平均精度（mAP）值为97.7%，98%,94.4%,分别。与基线YOLOv7相比，改进的CSCW-YOLOv7获得了精度，召回，mAP增加1.8%，1%,和2.1%，分别。同时,参数压缩了10.7%，减少了3.8MB，导致每秒浮点运算(FLOP)减少10%。梯度加权类激活图（Grad-CAM）可视化方法建议CSCW-YOLOv7可以学习一组更具代表性的特征，这些特征可以帮助在复杂的田间环境中更好地定位不同尺度的杂草。此外,将CSCW-YOLOv7的性能与广泛使用的深度学习模型进行了比较，结果表明，CSCW-YOLOv7具有更好的区分重叠杂草和小规模杂草的能力。总体结果表明，CSCW-YOLOv7是检测杂草的有前途的工具，具有很大的田间应用潜力。
