To solve the problems in online target detection on the embedded platform,such as high missed detection rate,low accuracy,and slow speed,a lightweight target recognition method of MobileNetV3-CenterNet is proposed.Thi...To solve the problems in online target detection on the embedded platform,such as high missed detection rate,low accuracy,and slow speed,a lightweight target recognition method of MobileNetV3-CenterNet is proposed.This method combines the anchor-free Centernet network with the MobileNetV3 small network and is trained on the University at Albany Detection and Tracking(UA-DETRAC)and the Pattern Analysis,Statical Modeling and Computational Learn-ing Visual Object Classes(PASCAL VOC)07+12 standard datasets.While reducing the scale of the network model,the MobileNetV3-CenterNet model shows a good balance in the accuracy and speed of target recognition and effectively solves the problems of missing detection of dense and small targets in online detection.To verify the recognition performance of the model,it is tested on 2683 images of the UA-DETRAC and PASCAL VOC 07+12 datasets,and compared with the analysis results of CenterNet-Deep Layer Aggregation(DLA)34,CenterNet-Residual Network(ResNet)18,CenterNet-MobileNetV3-large,You Only Look Once vision 3(YOLOv3),MobileNetV2-YOLOv3,Single Shot Multibox Detector(SSD),MobileNetV2-SSD and Faster region convolutional neural network(RCNN)models.The results show that the MobileNetV3-CenterNet model accurately rec-ognized the dense targets and small targets missed by other methods,and obtained a recognition accuracy of 99.4%with a running speed at 53(on a server)and 14(on an ipad)frame/s,respectively.The MobileNetV3-CenterNet lightweight target recognition method will provide effective technical support for the target recognition of deep learning networks in embedded platforms and online detection.展开更多
Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system ...Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. Methods By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of light-weighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. Results By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Conclusions Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache man-agement, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.展开更多
This paper takes the fuselage bracket of the quadrotor UAV as the object,combined with the characteristics of less constraints in the 3D printing process,achieves the optimal structure of the UAV body structure design...This paper takes the fuselage bracket of the quadrotor UAV as the object,combined with the characteristics of less constraints in the 3D printing process,achieves the optimal structure of the UAV body structure design form,using the inspire topology optimization analysis,with the optimization goal of maximizing the overall stiffness and the optimized structure was checked for strength and verified by 3D printing prototype.The results show that under the premise of meeting the design strength and stiffness requirements,the mass is reduced by 53.29%compared with the original design.The research shows that the application of topology optimization and 3D printing technology to the structural design of UAVs will achieve significant weight reduction effects,providing a feasible way to realize lightweight,complex and integrated design and manufacturing of components.展开更多
Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions requir...Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.展开更多
基金supported by Research and Development Project of Key Core Technology and Common Technology in Shanxi Province(No.2020XXX009).
文摘To solve the problems in online target detection on the embedded platform,such as high missed detection rate,low accuracy,and slow speed,a lightweight target recognition method of MobileNetV3-CenterNet is proposed.This method combines the anchor-free Centernet network with the MobileNetV3 small network and is trained on the University at Albany Detection and Tracking(UA-DETRAC)and the Pattern Analysis,Statical Modeling and Computational Learn-ing Visual Object Classes(PASCAL VOC)07+12 standard datasets.While reducing the scale of the network model,the MobileNetV3-CenterNet model shows a good balance in the accuracy and speed of target recognition and effectively solves the problems of missing detection of dense and small targets in online detection.To verify the recognition performance of the model,it is tested on 2683 images of the UA-DETRAC and PASCAL VOC 07+12 datasets,and compared with the analysis results of CenterNet-Deep Layer Aggregation(DLA)34,CenterNet-Residual Network(ResNet)18,CenterNet-MobileNetV3-large,You Only Look Once vision 3(YOLOv3),MobileNetV2-YOLOv3,Single Shot Multibox Detector(SSD),MobileNetV2-SSD and Faster region convolutional neural network(RCNN)models.The results show that the MobileNetV3-CenterNet model accurately rec-ognized the dense targets and small targets missed by other methods,and obtained a recognition accuracy of 99.4%with a running speed at 53(on a server)and 14(on an ipad)frame/s,respectively.The MobileNetV3-CenterNet lightweight target recognition method will provide effective technical support for the target recognition of deep learning networks in embedded platforms and online detection.
文摘Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. Methods By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of light-weighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. Results By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Conclusions Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache man-agement, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.
文摘This paper takes the fuselage bracket of the quadrotor UAV as the object,combined with the characteristics of less constraints in the 3D printing process,achieves the optimal structure of the UAV body structure design form,using the inspire topology optimization analysis,with the optimization goal of maximizing the overall stiffness and the optimized structure was checked for strength and verified by 3D printing prototype.The results show that under the premise of meeting the design strength and stiffness requirements,the mass is reduced by 53.29%compared with the original design.The research shows that the application of topology optimization and 3D printing technology to the structural design of UAVs will achieve significant weight reduction effects,providing a feasible way to realize lightweight,complex and integrated design and manufacturing of components.
文摘Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.
