This paper proposes SW-YOLO(StarNet Weighted-Conv YOLO),a lightweight human pose estimation network for edge devices.Current mainstream pose estimation algorithms are computationally inefficient and have poor feature ...This paper proposes SW-YOLO(StarNet Weighted-Conv YOLO),a lightweight human pose estimation network for edge devices.Current mainstream pose estimation algorithms are computationally inefficient and have poor feature capture capabilities for complex poses and occlusion scenarios.This work introduces a lightweight backbone architecture that integrates WConv(Weighted Convolution)and StarNet modules to address these issues.Leveraging StarNet’s superior capabilities in multi-level feature fusion and long-range dependency modeling,this architecture enhances the model’s spatial perception of human joint structures and contextual information integration.These improvements significantly enhance robustness in complex scenarios involving occlusion and deformation.Additionally,the introduction of WConv convolution operations,based on weight recalibration and receptive field optimization,dynamically adjusts feature importance during convolution.This reduces redundant computations while maintaining or enhancing feature representation capabilities at an extremely low computational cost.Consequently,SW-YOLO substantially reduces model complexity and inference latency while preserving high accuracy,significantly outperforming existing lightweight networks.展开更多
为解决因害虫尺度多样性导致其识别度相对较低的问题,本研究提出了一种基于PP-YOLO(PaddlePaddleYou Only Look once)的农业病虫害识别算法。选取2359个病虫害样本数据集,按照9∶1的比例进行训练集、测试集的划分;选择PP-YOLO模型进行...为解决因害虫尺度多样性导致其识别度相对较低的问题,本研究提出了一种基于PP-YOLO(PaddlePaddleYou Only Look once)的农业病虫害识别算法。选取2359个病虫害样本数据集,按照9∶1的比例进行训练集、测试集的划分;选择PP-YOLO模型进行病虫害监测,并利用平均精度mAP(mean average precision)指标进行模型精度评价;探讨PP-YOLO结合数据增强mixup、颜色扭曲法在病虫害中小目标检测上的适用性。结果表明,PP-YOLO模型在病虫害中小目标检测方面mAP达47.4%、26.5%;基于PP-YOLO模型结合数据增强mixup与颜色扭曲后在病虫害中小目标检测上mAP分别提升4.3%、2.9%。总之,PP-YOLO模型可有效检测识别农作物害虫,同时,数据增强mixup与颜色扭曲法可有效提升病虫害的数据样本指标。展开更多
为了综合分析YOLO(You Only Look Once)算法在提升交通安全性和效率方面的重要作用,从“人-车-路”3个核心要素的角度,对YOLO算法在交通目标检测中的发展和研究现状进行系统性地总结.概述了YOLO算法常用的评价指标,详细阐述了这些指标...为了综合分析YOLO(You Only Look Once)算法在提升交通安全性和效率方面的重要作用,从“人-车-路”3个核心要素的角度,对YOLO算法在交通目标检测中的发展和研究现状进行系统性地总结.概述了YOLO算法常用的评价指标,详细阐述了这些指标在交通场景中的实际意义.对YOLO算法的核心架构进行概述,追溯了该算法的发展历程,分析各个版本迭代中的优化和改进措施.从“人-车-路”3种交通目标的视角出发,梳理并论述了采用YOLO算法进行交通目标检测的研究现状及应用情况.分析目前YOLO算法在交通目标检测中存在的局限性和挑战,提出相应的改进方法,展望未来的研究重点,为道路交通的智能化发展提供了研究参考.展开更多
To avoid colliding with trees during its operation,a lawn mower robot must detect the trees.Existing tree detection methods suffer from low detection accuracy(missed detection)and the lack of a lightweight model.In th...To avoid colliding with trees during its operation,a lawn mower robot must detect the trees.Existing tree detection methods suffer from low detection accuracy(missed detection)and the lack of a lightweight model.In this study,a dataset of trees was constructed on the basis of a real lawn environment.According to the theory of channel incremental depthwise convolution and residual suppression,the Embedded-A module is proposed,which expands the depth of the feature map twice to form a residual structure to improve the lightweight degree of the model.According to residual fusion theory,the Embedded-B module is proposed,which improves the accuracy of feature-map downsampling by depthwise convolution and pooling fusion.The Embedded YOLO object detection network is formed by stacking the embedded modules and the fusion of feature maps of different resolutions.Experimental results on the testing set show that the Embedded YOLO tree detection algorithm has 84.17%and 69.91%average precision values respectively for trunk and spherical tree,and 77.04% mean average precision value.The number of convolution parameters is 1.78×10^(6),and the calculation amount is 3.85 billion float operations per second.The size of weight file is 7.11MB,and the detection speed can reach 179 frame/s.This study provides a theoretical basis for the lightweight application of the object detection algorithm based on deep learning for lawn mower robots.展开更多
目前在铝材表面缺陷检测领域,常用的检测模型存在检测精度不高、实时性不强和参数量大等问题。针对上述问题,对目标检测模型YOLOv8做了改进。首先,该模型使用自研的动态可变形卷积模块取代原有最后一层C2f模块;其次,将RT-DETR(real-time...目前在铝材表面缺陷检测领域,常用的检测模型存在检测精度不高、实时性不强和参数量大等问题。针对上述问题,对目标检测模型YOLOv8做了改进。首先,该模型使用自研的动态可变形卷积模块取代原有最后一层C2f模块;其次,将RT-DETR(real-time detection transformer)模型中的检测头移植到新模型中,利用Transformer中解码器的思想,省去了模型后处理的步骤;最后,将NWD(normalized Wasserstein distance)loss和WIoUv3(wise-intersection over union version 3)loss结合作为改进后模型的回归损失函数,使模型动态筛选锚框,解决IoU对于不同尺寸缺陷类型敏感度不同导致的标签分配不准确的问题。改进之后的模型相比于基线模型YOLOv8,平均精度提高了3.8%,每秒处理帧数提高至92 f/s,同时在钢材表面缺陷检测数据集和太阳能电池板表面缺陷检测数据集上也具有很强的鲁棒性,新模型在实时检测、实际部署中具有很大优势。展开更多
文摘This paper proposes SW-YOLO(StarNet Weighted-Conv YOLO),a lightweight human pose estimation network for edge devices.Current mainstream pose estimation algorithms are computationally inefficient and have poor feature capture capabilities for complex poses and occlusion scenarios.This work introduces a lightweight backbone architecture that integrates WConv(Weighted Convolution)and StarNet modules to address these issues.Leveraging StarNet’s superior capabilities in multi-level feature fusion and long-range dependency modeling,this architecture enhances the model’s spatial perception of human joint structures and contextual information integration.These improvements significantly enhance robustness in complex scenarios involving occlusion and deformation.Additionally,the introduction of WConv convolution operations,based on weight recalibration and receptive field optimization,dynamically adjusts feature importance during convolution.This reduces redundant computations while maintaining or enhancing feature representation capabilities at an extremely low computational cost.Consequently,SW-YOLO substantially reduces model complexity and inference latency while preserving high accuracy,significantly outperforming existing lightweight networks.
文摘为解决因害虫尺度多样性导致其识别度相对较低的问题,本研究提出了一种基于PP-YOLO(PaddlePaddleYou Only Look once)的农业病虫害识别算法。选取2359个病虫害样本数据集,按照9∶1的比例进行训练集、测试集的划分;选择PP-YOLO模型进行病虫害监测,并利用平均精度mAP(mean average precision)指标进行模型精度评价;探讨PP-YOLO结合数据增强mixup、颜色扭曲法在病虫害中小目标检测上的适用性。结果表明,PP-YOLO模型在病虫害中小目标检测方面mAP达47.4%、26.5%;基于PP-YOLO模型结合数据增强mixup与颜色扭曲后在病虫害中小目标检测上mAP分别提升4.3%、2.9%。总之,PP-YOLO模型可有效检测识别农作物害虫,同时,数据增强mixup与颜色扭曲法可有效提升病虫害的数据样本指标。
文摘为了综合分析YOLO(You Only Look Once)算法在提升交通安全性和效率方面的重要作用,从“人-车-路”3个核心要素的角度,对YOLO算法在交通目标检测中的发展和研究现状进行系统性地总结.概述了YOLO算法常用的评价指标,详细阐述了这些指标在交通场景中的实际意义.对YOLO算法的核心架构进行概述,追溯了该算法的发展历程,分析各个版本迭代中的优化和改进措施.从“人-车-路”3种交通目标的视角出发,梳理并论述了采用YOLO算法进行交通目标检测的研究现状及应用情况.分析目前YOLO算法在交通目标检测中存在的局限性和挑战,提出相应的改进方法,展望未来的研究重点,为道路交通的智能化发展提供了研究参考.
基金the National Natural Science Foundation of China (No.51275223)。
文摘To avoid colliding with trees during its operation,a lawn mower robot must detect the trees.Existing tree detection methods suffer from low detection accuracy(missed detection)and the lack of a lightweight model.In this study,a dataset of trees was constructed on the basis of a real lawn environment.According to the theory of channel incremental depthwise convolution and residual suppression,the Embedded-A module is proposed,which expands the depth of the feature map twice to form a residual structure to improve the lightweight degree of the model.According to residual fusion theory,the Embedded-B module is proposed,which improves the accuracy of feature-map downsampling by depthwise convolution and pooling fusion.The Embedded YOLO object detection network is formed by stacking the embedded modules and the fusion of feature maps of different resolutions.Experimental results on the testing set show that the Embedded YOLO tree detection algorithm has 84.17%and 69.91%average precision values respectively for trunk and spherical tree,and 77.04% mean average precision value.The number of convolution parameters is 1.78×10^(6),and the calculation amount is 3.85 billion float operations per second.The size of weight file is 7.11MB,and the detection speed can reach 179 frame/s.This study provides a theoretical basis for the lightweight application of the object detection algorithm based on deep learning for lawn mower robots.
文摘目前在铝材表面缺陷检测领域,常用的检测模型存在检测精度不高、实时性不强和参数量大等问题。针对上述问题,对目标检测模型YOLOv8做了改进。首先,该模型使用自研的动态可变形卷积模块取代原有最后一层C2f模块;其次,将RT-DETR(real-time detection transformer)模型中的检测头移植到新模型中,利用Transformer中解码器的思想,省去了模型后处理的步骤;最后,将NWD(normalized Wasserstein distance)loss和WIoUv3(wise-intersection over union version 3)loss结合作为改进后模型的回归损失函数,使模型动态筛选锚框,解决IoU对于不同尺寸缺陷类型敏感度不同导致的标签分配不准确的问题。改进之后的模型相比于基线模型YOLOv8,平均精度提高了3.8%,每秒处理帧数提高至92 f/s,同时在钢材表面缺陷检测数据集和太阳能电池板表面缺陷检测数据集上也具有很强的鲁棒性,新模型在实时检测、实际部署中具有很大优势。
