The rapid pace of urban development has resulted in the widespread presence of construction equipment andincreasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safeope...The rapid pace of urban development has resulted in the widespread presence of construction equipment andincreasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safeoperation of the power grid.Machine vision technology, particularly object recognition technology, has beenwidelyemployed to identify foreign objects in transmission line images. Despite its wide application, the technique faceslimitations due to the complex environmental background and other auxiliary factors. To address these challenges,this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replacedwith a spatial-depth convolution (SPD-Conv) module, aiming to improve the algorithm’s efficacy in recognizinglow-resolution and small-size objects. The algorithm’s feature extraction network is improved by using a LargeSelective Kernel (LSK) attention mechanism, which enhances the ability to extract relevant features. Additionally,the SIoU Loss function is used instead of the Complete Intersection over Union (CIoU) Loss to facilitate fasterconvergence of the algorithm. Through experimental verification, the improved YOLOv8n model achieves adetection accuracy of 88.8% on the test set. The recognition accuracy of cranes is improved by 2.9%, which isa significant enhancement compared to the unimproved algorithm. This improvement effectively enhances theaccuracy of recognizing foreign objects on transmission lines and proves the effectiveness of the new algorithm.展开更多
针对乒乓球目标检测方法易受环境、光线、速度等多种因素干扰导致精度和实时性不佳的问题,提出了一种基于YOLOv5s框架的轻量化乒乓球目标检测算法——SYOLO5(Shuffle-YOLOv5s)。首先,采用改进的ShuffleNetV2网络单元组合重构YOLOv5s主...针对乒乓球目标检测方法易受环境、光线、速度等多种因素干扰导致精度和实时性不佳的问题,提出了一种基于YOLOv5s框架的轻量化乒乓球目标检测算法——SYOLO5(Shuffle-YOLOv5s)。首先,采用改进的ShuffleNetV2网络单元组合重构YOLOv5s主干网络,提高特征提取速度;其次,在特征融合的过程中引入高效通道注意力(ECA)机制,有效提升模型的检测性能;接着,采用SIoU Loss(S-Intersection over Union)作为定位损失函数提升网络的收敛速度和定位精度;最后,贴合乒乓球小尺寸的特点,采用双尺度目标检测,进一步提高模型推理速度。实验结果表明,所提算法与YOLOv5s相比,参数量和计算量分别减少了80%和60%,精确率提升了1.9个百分点。展开更多
SiamRPN算法采用Ln范数损失训练边界框预测,未考虑预测框与真值框间交并比(inersection over union,IoU)的关系,导致准确性不足。针对该问题,提出一种结合IoU损失的SiamRPN目标跟踪改进算法。设计了IoU-smooth L1范数联合优化模块,对候...SiamRPN算法采用Ln范数损失训练边界框预测,未考虑预测框与真值框间交并比(inersection over union,IoU)的关系,导致准确性不足。针对该问题,提出一种结合IoU损失的SiamRPN目标跟踪改进算法。设计了IoU-smooth L1范数联合优化模块,对候选正样本进行IoU损失与smooth L1范数损失的联合优化;依据回归预测结果,用预测框与真值框的IoU作为权重对正样本进行加权分类预测,增加正样本间的区分度,同时确保分类预测与回归预测的关联性。对比实验结果表明:本文所提改进算法能有效提升跟踪性能。展开更多
基金the Natural Science Foundation of Shandong Province(ZR2021QE289)State Key Laboratory of Electrical Insulation and Power Equipment(EIPE22201).
文摘The rapid pace of urban development has resulted in the widespread presence of construction equipment andincreasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safeoperation of the power grid.Machine vision technology, particularly object recognition technology, has beenwidelyemployed to identify foreign objects in transmission line images. Despite its wide application, the technique faceslimitations due to the complex environmental background and other auxiliary factors. To address these challenges,this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replacedwith a spatial-depth convolution (SPD-Conv) module, aiming to improve the algorithm’s efficacy in recognizinglow-resolution and small-size objects. The algorithm’s feature extraction network is improved by using a LargeSelective Kernel (LSK) attention mechanism, which enhances the ability to extract relevant features. Additionally,the SIoU Loss function is used instead of the Complete Intersection over Union (CIoU) Loss to facilitate fasterconvergence of the algorithm. Through experimental verification, the improved YOLOv8n model achieves adetection accuracy of 88.8% on the test set. The recognition accuracy of cranes is improved by 2.9%, which isa significant enhancement compared to the unimproved algorithm. This improvement effectively enhances theaccuracy of recognizing foreign objects on transmission lines and proves the effectiveness of the new algorithm.
文摘针对乒乓球目标检测方法易受环境、光线、速度等多种因素干扰导致精度和实时性不佳的问题,提出了一种基于YOLOv5s框架的轻量化乒乓球目标检测算法——SYOLO5(Shuffle-YOLOv5s)。首先,采用改进的ShuffleNetV2网络单元组合重构YOLOv5s主干网络,提高特征提取速度;其次,在特征融合的过程中引入高效通道注意力(ECA)机制,有效提升模型的检测性能;接着,采用SIoU Loss(S-Intersection over Union)作为定位损失函数提升网络的收敛速度和定位精度;最后,贴合乒乓球小尺寸的特点,采用双尺度目标检测,进一步提高模型推理速度。实验结果表明,所提算法与YOLOv5s相比,参数量和计算量分别减少了80%和60%,精确率提升了1.9个百分点。
文摘SiamRPN算法采用Ln范数损失训练边界框预测,未考虑预测框与真值框间交并比(inersection over union,IoU)的关系,导致准确性不足。针对该问题,提出一种结合IoU损失的SiamRPN目标跟踪改进算法。设计了IoU-smooth L1范数联合优化模块,对候选正样本进行IoU损失与smooth L1范数损失的联合优化;依据回归预测结果,用预测框与真值框的IoU作为权重对正样本进行加权分类预测,增加正样本间的区分度,同时确保分类预测与回归预测的关联性。对比实验结果表明:本文所提改进算法能有效提升跟踪性能。
