This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagno...This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.展开更多
自动抄表(Automatic Meter Reading,AMR)在变电站电表读数中具有重要的应用价值。近年来,深度学习图像识别技术在AMR领域取得了显著进展。然而,现有方法大多依赖于计数器检测、分割和识别的3阶段流程,存在复杂性和效率方面的问题。为提...自动抄表(Automatic Meter Reading,AMR)在变电站电表读数中具有重要的应用价值。近年来,深度学习图像识别技术在AMR领域取得了显著进展。然而,现有方法大多依赖于计数器检测、分割和识别的3阶段流程,存在复杂性和效率方面的问题。为提升AMR的准确性与效率,首次将序列到序列(Sequence-to-Sequence,Seq2Seq)架构引入该任务,结合YOLOv5进行计数器检测,并利用Seq2Seq架构直接识别计数器,省略了传统流程中的计数器分割步骤。此外,还提出改进注意力机制的Seq2Seq架构,以优化信息传递与特征对齐。在UFPR-AMR公开数据集上的实验表明,改进方法的准确率达到了92.5%,比原方法提升了1.25%,这一结果验证了所提出的方法在AMR任务中的有效性。展开更多
文摘This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.
文摘自动抄表(Automatic Meter Reading,AMR)在变电站电表读数中具有重要的应用价值。近年来,深度学习图像识别技术在AMR领域取得了显著进展。然而,现有方法大多依赖于计数器检测、分割和识别的3阶段流程,存在复杂性和效率方面的问题。为提升AMR的准确性与效率,首次将序列到序列(Sequence-to-Sequence,Seq2Seq)架构引入该任务,结合YOLOv5进行计数器检测,并利用Seq2Seq架构直接识别计数器,省略了传统流程中的计数器分割步骤。此外,还提出改进注意力机制的Seq2Seq架构,以优化信息传递与特征对齐。在UFPR-AMR公开数据集上的实验表明,改进方法的准确率达到了92.5%,比原方法提升了1.25%,这一结果验证了所提出的方法在AMR任务中的有效性。
