In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of ...In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.展开更多
为提高自然环境中玉米害虫识别的准确性,开发一种基于优化YOLOv8的深度学习模型YOLOv8-LAP。该模型将大型可分离卷积核注意力(LSKA)机制引入特征融合模块空间快速金字塔池化(SPPF),增强多尺度特征提取能力,提升检测性能。针对玉米害虫...为提高自然环境中玉米害虫识别的准确性,开发一种基于优化YOLOv8的深度学习模型YOLOv8-LAP。该模型将大型可分离卷积核注意力(LSKA)机制引入特征融合模块空间快速金字塔池化(SPPF),增强多尺度特征提取能力,提升检测性能。针对玉米害虫图像检测中小目标难以捕捉、背景复杂和光照变化等挑战,在主干网络中加入AFGC(Attention for Fine-Grained Categorization)层,以进一步增强图像特征提取的效果,提升模型的泛化能力和鲁棒性。为保证实时检测和模型轻量化,引入可编程梯度信息(PGI)技术,通过辅助监督优化训练过程,减少参数并加速推理。在9种常见玉米害虫的检测中,YOLOv8-LAP模型的平均精度均值(mAP0.5)达到了95.7%,相较于原始YOLOv8模型提高了4.9个百分点。此外,为验证YOLOv8-LAP模型的效果,开发一款基于PySide6的应用程序,该应用拥有用户友好的图形用户界面(GUI),具有实时图像处理和视频分析功能,并支持静态图像、动态视频和摄像头实时目标检测。可见,YOLOv8-LAP模型在降低漏检率和误检率方面表现突出,目标定位更精准,适用于自然环境下的玉米害虫识别,并为精准施药提供技术支持。展开更多
基金supported by the National Natural Science Foundation of China(71401052)the Fundamental Research Funds for the Central Universities(2019B19514)。
文摘In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.
文摘为提高自然环境中玉米害虫识别的准确性,开发一种基于优化YOLOv8的深度学习模型YOLOv8-LAP。该模型将大型可分离卷积核注意力(LSKA)机制引入特征融合模块空间快速金字塔池化(SPPF),增强多尺度特征提取能力,提升检测性能。针对玉米害虫图像检测中小目标难以捕捉、背景复杂和光照变化等挑战,在主干网络中加入AFGC(Attention for Fine-Grained Categorization)层,以进一步增强图像特征提取的效果,提升模型的泛化能力和鲁棒性。为保证实时检测和模型轻量化,引入可编程梯度信息(PGI)技术,通过辅助监督优化训练过程,减少参数并加速推理。在9种常见玉米害虫的检测中,YOLOv8-LAP模型的平均精度均值(mAP0.5)达到了95.7%,相较于原始YOLOv8模型提高了4.9个百分点。此外,为验证YOLOv8-LAP模型的效果,开发一款基于PySide6的应用程序,该应用拥有用户友好的图形用户界面(GUI),具有实时图像处理和视频分析功能,并支持静态图像、动态视频和摄像头实时目标检测。可见,YOLOv8-LAP模型在降低漏检率和误检率方面表现突出,目标定位更精准,适用于自然环境下的玉米害虫识别,并为精准施药提供技术支持。
