Conference Theme Advanced Technologies for Emergency Planning and ResponseThe 2008 IEEE International Conference on Networking, Sensing and Control will be held in Sanya,China. The main theme of the conference is adva...Conference Theme Advanced Technologies for Emergency Planning and ResponseThe 2008 IEEE International Conference on Networking, Sensing and Control will be held in Sanya,China. The main theme of the conference is advanced technologies for emergency planning and re-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
To address the problems of large number of parameters and high complexity of calculation in the current steel surface defect detection model,a steel surface defect lightweight algorithm CGV-YOLO based on the improveme...To address the problems of large number of parameters and high complexity of calculation in the current steel surface defect detection model,a steel surface defect lightweight algorithm CGV-YOLO based on the improvement of YOLOv8 was proposed in this study.Firstly,in the process of optimizing the network architecture,the algorithm designed the FRC module and embeds it in the backbone network.Then,the GSConv convolution was employed to construct the Slim-neck network architecture,which further reduces computational load while maintaining model accuracy.Finally,the optimized CBAM replaced the C2f module in the YOLOv8 backbone network,reducing both model parameters and computational load.Based on the database of NEU-DET and BSD,the F1-Score of the CGV-YOLO algorithm is improved by 1.3%and 1.1%respectively compared with the baseline model.Based on the database of NEU-DET,the Params and computational complexity of the model are reduced by 30.6%and 35.3%respectively against the baseline.The results demonstrated that the proposed algorithm drastically reduces the number of parameters and computational cost with the maintenance of the accuracy of the model and realizes the lightweight effect.展开更多
本文以玉米叶斑病检测为研究对象,针对现有目标检测模型普遍存在的计算复杂度高、部署困难等问题,基于you only look once version 8(YOLOv8)算法提出了一种改进方案.构建轻量化注意力模块提升特征提取能力,引入SIoU损失函数优化目标框...本文以玉米叶斑病检测为研究对象,针对现有目标检测模型普遍存在的计算复杂度高、部署困难等问题,基于you only look once version 8(YOLOv8)算法提出了一种改进方案.构建轻量化注意力模块提升特征提取能力,引入SIoU损失函数优化目标框定位精度,采用基于BatchNorm层的模型剪枝策略降低计算复杂度.在RobFlow玉米病虫害数据集上的实验结果表明,改进后的模型检测精度达到88.8%,较原始YOLOv8算法提升了0.7个百分点;同时模型参数量和计算量分别减少33.1%和31.4%,推理速度提升20.5%.该方法在保持较高检测精度的同时,显著提升了效率,为农作物病虫害智能检测提供了新的技术思路.展开更多
Accurate vehicle detection is essential for autonomous driving,traffic monitoring,and intelligent transportation systems.This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module,Convolutional B...Accurate vehicle detection is essential for autonomous driving,traffic monitoring,and intelligent transportation systems.This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module,Convolutional Block Attention Module(CBAM),and Deformable Convolutional Networks v2(DCNv2).The Ghost Module streamlines feature generation to reduce redundancy,CBAM applies channel and spatial attention to improve feature focus,and DCNv2 enables adaptability to geometric variations in vehicle shapes.These components work together to improve both accuracy and computational efficiency.Evaluated on the KITTI dataset,the proposed model achieves 95.4%mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% precision,93.7% recall,and a 94.93%F1-score.Comparative analysis with seven state-of-the-art detectors demonstrates consistent superiority in key performance metrics.An ablation study is also conducted to quantify the individual and combined contributions of GhostModule,CBAM,and DCNv2,highlighting their effectiveness in improving detection performance.By addressing feature redundancy,attention refinement,and spatial adaptability,the proposed model offers a robust and scalable solution for vehicle detection across diverse traffic scenarios.展开更多
为推动输电线路智能化巡检模式,本文针对人机协同巡检模式下的图像差异大及干扰因素多等问题,提出一种轻量化改进型YOLOv8(you only look once version 8)的多类别绝缘子缺陷检测算法。首先在特征提取网络中融合可变形大核注意力的同时...为推动输电线路智能化巡检模式,本文针对人机协同巡检模式下的图像差异大及干扰因素多等问题,提出一种轻量化改进型YOLOv8(you only look once version 8)的多类别绝缘子缺陷检测算法。首先在特征提取网络中融合可变形大核注意力的同时进行轻量化,提升网络对不同目标轮廓与尺寸的适用性;其次引入渐进的特征融合策略以改善不同层次特征间的语义差距,提高网络的检测精度;并设计轻量化非对称检测头,进一步减少参数冗余;最后改进边框损失函数有效降低由密集遮挡造成的漏检和误检数量。实验结果表明,本文算法相较于原算法检测精度提升了7.7%,参数量和计算量分别减少了26.4%和30.2%,并在密集、遮挡、多类别目标缺陷检测中的评价指标均领先于当前主流的几类目标检测算法,显著提高了复杂环境下的多类别绝缘子缺陷检测,实现了检测精度和速度的双重提升。展开更多
Tight sandstone reservoirs have strong heterogeneity and complex gas-water relationship,causing diffi culty in quantitatively predicting water saturation.Deep learning,combined with rock physics analysis and geostatis...Tight sandstone reservoirs have strong heterogeneity and complex gas-water relationship,causing diffi culty in quantitatively predicting water saturation.Deep learning,combined with rock physics analysis and geostatistics theory,was used to predict water saturation in tight sandstone,focusing on the P_(sh)^(8) in the GFZ area of the Ordos Basin.Results show that:Starting with actual wells where porosity and saturation results are obtained from log interpretations,the relationship between reservoir parameters(porosity and saturation)and elastic properties(P-wave velocity,S-wave velocity,and density)is established through the development of a rock physics model suitable for the region.Under the constraints of geostatistical laws,such as background trends of elastic and reservoir parameters and the vertical variations in logging curves,reservoir conditions(including porosity,saturation,and thickness)are simulated to generate numerous pseudowells and corresponding seismic gathers modeled using the Zoeppritz equation.A convolution neural network is used to train the target curve and predict the target body.The predicted water saturation of the P_(sh)^(8) shows strong agreement with the results from two blind wells,providing a reliable basis for understanding the water saturation(Sw)of tight sandstone.展开更多
文摘Conference Theme Advanced Technologies for Emergency Planning and ResponseThe 2008 IEEE International Conference on Networking, Sensing and Control will be held in Sanya,China. The main theme of the conference is advanced technologies for emergency planning and re-
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.
文摘To address the problems of large number of parameters and high complexity of calculation in the current steel surface defect detection model,a steel surface defect lightweight algorithm CGV-YOLO based on the improvement of YOLOv8 was proposed in this study.Firstly,in the process of optimizing the network architecture,the algorithm designed the FRC module and embeds it in the backbone network.Then,the GSConv convolution was employed to construct the Slim-neck network architecture,which further reduces computational load while maintaining model accuracy.Finally,the optimized CBAM replaced the C2f module in the YOLOv8 backbone network,reducing both model parameters and computational load.Based on the database of NEU-DET and BSD,the F1-Score of the CGV-YOLO algorithm is improved by 1.3%and 1.1%respectively compared with the baseline model.Based on the database of NEU-DET,the Params and computational complexity of the model are reduced by 30.6%and 35.3%respectively against the baseline.The results demonstrated that the proposed algorithm drastically reduces the number of parameters and computational cost with the maintenance of the accuracy of the model and realizes the lightweight effect.
文摘本文以玉米叶斑病检测为研究对象,针对现有目标检测模型普遍存在的计算复杂度高、部署困难等问题,基于you only look once version 8(YOLOv8)算法提出了一种改进方案.构建轻量化注意力模块提升特征提取能力,引入SIoU损失函数优化目标框定位精度,采用基于BatchNorm层的模型剪枝策略降低计算复杂度.在RobFlow玉米病虫害数据集上的实验结果表明,改进后的模型检测精度达到88.8%,较原始YOLOv8算法提升了0.7个百分点;同时模型参数量和计算量分别减少33.1%和31.4%,推理速度提升20.5%.该方法在保持较高检测精度的同时,显著提升了效率,为农作物病虫害智能检测提供了新的技术思路.
文摘Accurate vehicle detection is essential for autonomous driving,traffic monitoring,and intelligent transportation systems.This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module,Convolutional Block Attention Module(CBAM),and Deformable Convolutional Networks v2(DCNv2).The Ghost Module streamlines feature generation to reduce redundancy,CBAM applies channel and spatial attention to improve feature focus,and DCNv2 enables adaptability to geometric variations in vehicle shapes.These components work together to improve both accuracy and computational efficiency.Evaluated on the KITTI dataset,the proposed model achieves 95.4%mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% precision,93.7% recall,and a 94.93%F1-score.Comparative analysis with seven state-of-the-art detectors demonstrates consistent superiority in key performance metrics.An ablation study is also conducted to quantify the individual and combined contributions of GhostModule,CBAM,and DCNv2,highlighting their effectiveness in improving detection performance.By addressing feature redundancy,attention refinement,and spatial adaptability,the proposed model offers a robust and scalable solution for vehicle detection across diverse traffic scenarios.
文摘为推动输电线路智能化巡检模式,本文针对人机协同巡检模式下的图像差异大及干扰因素多等问题,提出一种轻量化改进型YOLOv8(you only look once version 8)的多类别绝缘子缺陷检测算法。首先在特征提取网络中融合可变形大核注意力的同时进行轻量化,提升网络对不同目标轮廓与尺寸的适用性;其次引入渐进的特征融合策略以改善不同层次特征间的语义差距,提高网络的检测精度;并设计轻量化非对称检测头,进一步减少参数冗余;最后改进边框损失函数有效降低由密集遮挡造成的漏检和误检数量。实验结果表明,本文算法相较于原算法检测精度提升了7.7%,参数量和计算量分别减少了26.4%和30.2%,并在密集、遮挡、多类别目标缺陷检测中的评价指标均领先于当前主流的几类目标检测算法,显著提高了复杂环境下的多类别绝缘子缺陷检测,实现了检测精度和速度的双重提升。
基金Supported by:CNPC Major Project "Research on Key Technologies for Enhanced Oil Recovery in Tight Sandstone Gas Reservoirs"(No. 2023ZZ25)Gansu Provincial Science and Technology Major Project"Research and Application of Key Technologies for Geophysical Prediction of Natural Gas Reservoirs in Longdong Area"(No. 23ZDGA004)PetroChina Changqing Oilfield Company'Qingshimao gas field water-bearing gas reservoir 3D seismic fine interpretation and well position support'(No.2023QCPJ33)。
文摘Tight sandstone reservoirs have strong heterogeneity and complex gas-water relationship,causing diffi culty in quantitatively predicting water saturation.Deep learning,combined with rock physics analysis and geostatistics theory,was used to predict water saturation in tight sandstone,focusing on the P_(sh)^(8) in the GFZ area of the Ordos Basin.Results show that:Starting with actual wells where porosity and saturation results are obtained from log interpretations,the relationship between reservoir parameters(porosity and saturation)and elastic properties(P-wave velocity,S-wave velocity,and density)is established through the development of a rock physics model suitable for the region.Under the constraints of geostatistical laws,such as background trends of elastic and reservoir parameters and the vertical variations in logging curves,reservoir conditions(including porosity,saturation,and thickness)are simulated to generate numerous pseudowells and corresponding seismic gathers modeled using the Zoeppritz equation.A convolution neural network is used to train the target curve and predict the target body.The predicted water saturation of the P_(sh)^(8) shows strong agreement with the results from two blind wells,providing a reliable basis for understanding the water saturation(Sw)of tight sandstone.
