作为高铁牵引供电系统的重要组成部分,接触网系统承担着向动车组传输电能的重要功能.实际工程运营表明,受弓网交互产生的持续冲击以及外部环境的影响,接触网支撑部件可能会出现“松、脱、断、裂”等缺陷,导致接触网结构可靠性下降,严重...作为高铁牵引供电系统的重要组成部分,接触网系统承担着向动车组传输电能的重要功能.实际工程运营表明,受弓网交互产生的持续冲击以及外部环境的影响,接触网支撑部件可能会出现“松、脱、断、裂”等缺陷,导致接触网结构可靠性下降,严重影响接触网系统稳定运行.因此,及时精确定位接触网支撑部件(CSCs),对保障高铁安全运行和完善接触网检修维护策略具有重大意义.然而,CSCs的检测通常面临着零部件种类多、尺度差异大、部分零部件微小的问题.针对以上问题,本文提出一种基于多尺度融合金字塔焦点网络的接触网零部件检测算法,将平衡模块和特征金字塔模块相结合,提高对小目标的检测性能.首先,设计了可分离残差金字塔聚合模块(SRPAM),用于优化模型多尺度特征提取能力、扩大感受野,缓解CSCs检测的多尺度问题;其次,设计了一种基于平衡特征金字塔的路径聚合网络(PA-BFPN),用于提升跨层特征融合效率和小目标检测性能.最后,通过对比试验、可视化实验和消融实验证明了所提方法的有效性和优越性.其中,所提的MFPFCOS在CSCs数据集上的检测精度(mAP)能够在达到48.6%的同时,实现30的FLOPs(Floating point operations per second),表明所提方法能够在检测精度和检测速度之间保持良好的平衡.展开更多
Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially...Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.展开更多
文摘作为高铁牵引供电系统的重要组成部分,接触网系统承担着向动车组传输电能的重要功能.实际工程运营表明,受弓网交互产生的持续冲击以及外部环境的影响,接触网支撑部件可能会出现“松、脱、断、裂”等缺陷,导致接触网结构可靠性下降,严重影响接触网系统稳定运行.因此,及时精确定位接触网支撑部件(CSCs),对保障高铁安全运行和完善接触网检修维护策略具有重大意义.然而,CSCs的检测通常面临着零部件种类多、尺度差异大、部分零部件微小的问题.针对以上问题,本文提出一种基于多尺度融合金字塔焦点网络的接触网零部件检测算法,将平衡模块和特征金字塔模块相结合,提高对小目标的检测性能.首先,设计了可分离残差金字塔聚合模块(SRPAM),用于优化模型多尺度特征提取能力、扩大感受野,缓解CSCs检测的多尺度问题;其次,设计了一种基于平衡特征金字塔的路径聚合网络(PA-BFPN),用于提升跨层特征融合效率和小目标检测性能.最后,通过对比试验、可视化实验和消融实验证明了所提方法的有效性和优越性.其中,所提的MFPFCOS在CSCs数据集上的检测精度(mAP)能够在达到48.6%的同时,实现30的FLOPs(Floating point operations per second),表明所提方法能够在检测精度和检测速度之间保持良好的平衡.
文摘Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.
