金属表面锈蚀的检测是激光智能清洗系统实现实时质量评估的关键技术,但传统视觉检测方法对小尺度锈蚀颗粒的识别能力不足。基于YOLO(You Only Look Once)算法,提出一种改进模型。该模型在骨干网络中嵌入卷积块注意力模块(Convolutional ...金属表面锈蚀的检测是激光智能清洗系统实现实时质量评估的关键技术,但传统视觉检测方法对小尺度锈蚀颗粒的识别能力不足。基于YOLO(You Only Look Once)算法,提出一种改进模型。该模型在骨干网络中嵌入卷积块注意力模块(Convolutional Block Attention Module,CBAM),增强复杂背景下的特征鉴别能力;设计基于部分卷积的跨阶段部分金字塔连接(Cross Stage Partial with Pyramid Concatenation,CSPPC)模块替代带聚焦机制的第二代跨阶段局部网络(Cross Stage Partial Network 2 with Focus,C2f)模块,减少了3.11%的参数量,计算量浮点数降低了6.64%;采用聚焦高效交并比(Focal and Efficient Intersection over Union,Focal-EIoU)损失函数,优化边界框的回归过程,并有效缓解了正样本和负样本之间的不平衡状况。结果表明,该YOLOv8-CCF(YOLOv8-CBAM-CSPPC-Focal-EIoU)算法改进模型在自制数据集上,在95%交并比阈值下的平均精度均值(mean Average Precision at 95%Intersection over Union,mAP@95%)达到0.96902,较原模型提升了5.003%,参数量减少至21.3万,检测速度达500 f/s,显著改善了小目标漏检问题。该模型为金属表面锈蚀的实时检测与激光自动化除锈提供了有效解决方案。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
文摘金属表面锈蚀的检测是激光智能清洗系统实现实时质量评估的关键技术,但传统视觉检测方法对小尺度锈蚀颗粒的识别能力不足。基于YOLO(You Only Look Once)算法,提出一种改进模型。该模型在骨干网络中嵌入卷积块注意力模块(Convolutional Block Attention Module,CBAM),增强复杂背景下的特征鉴别能力;设计基于部分卷积的跨阶段部分金字塔连接(Cross Stage Partial with Pyramid Concatenation,CSPPC)模块替代带聚焦机制的第二代跨阶段局部网络(Cross Stage Partial Network 2 with Focus,C2f)模块,减少了3.11%的参数量,计算量浮点数降低了6.64%;采用聚焦高效交并比(Focal and Efficient Intersection over Union,Focal-EIoU)损失函数,优化边界框的回归过程,并有效缓解了正样本和负样本之间的不平衡状况。结果表明,该YOLOv8-CCF(YOLOv8-CBAM-CSPPC-Focal-EIoU)算法改进模型在自制数据集上,在95%交并比阈值下的平均精度均值(mean Average Precision at 95%Intersection over Union,mAP@95%)达到0.96902,较原模型提升了5.003%,参数量减少至21.3万,检测速度达500 f/s,显著改善了小目标漏检问题。该模型为金属表面锈蚀的实时检测与激光自动化除锈提供了有效解决方案。
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.
