The judgment of gear failure is based on the pitting area ratio of gear.Traditional gear pitting calculation method mainly rely on manual visual inspection.This method is greatly affected by human factors,and is great...The judgment of gear failure is based on the pitting area ratio of gear.Traditional gear pitting calculation method mainly rely on manual visual inspection.This method is greatly affected by human factors,and is greatly affected by the working experience,training degree and fatigue degree of the detection personnel,so the detection results may be biased.The non-contact computer vision measurement can carry out non-destructive testing and monitoring under the working condition of the machine,and has high detection accuracy.To improve the measurement accuracy of gear pitting,a novel multi-scale splicing attention U-Net(MSSA U-Net)is explored in this study.An image splicing module is first proposed for concatenating the output feature maps of multiple convolutional layers into a splicing feature map with more semantic information.Then,an attention module is applied to select the key features of the splicing feature map.Given that MSSA U-Net adequately uses multi-scale semantic features,it has better segmentation performance on irregular small objects than U-Net and attention U-Net.On the basis of the designed visual detection platform and MSSA U-Net,a methodology for measuring the area ratio of gear pitting is proposed.With three datasets,experimental results show that MSSA U-Net is superior to existing typical image segmentation methods and can accurately segment different levels of pitting due to its strong segmentation ability.Therefore,the proposed methodology can be effectively applied in measuring the pitting area ratio and determining the level of gear pitting.展开更多
Although convolutional neural networks have become the mainstream segmentation model,the locality of convolution makes them cannot well learn global and long-range semantic information.To further improve the performan...Although convolutional neural networks have become the mainstream segmentation model,the locality of convolution makes them cannot well learn global and long-range semantic information.To further improve the performance of segmentation models,we propose U-shaped vision Transformer(UsViT),a model based on Transformer and convolution.Specifically,residual Transformer blocks are designed in the encoder of UsViT,which take advantages of residual network and Transformer backbone at the same time.What is more,transpositions in each Transformer layer achieve the information interaction between spatial locations and feature channels,enhancing the capability of feature learning.In the decoder,for enhancing receptive field,different dilation rates are introduced to each convolutional layer.In addition,residual connections are applied to make the information propagation smoother when training the model.We first verify the superiority of UsViT on automatic portrait matting public dataset,which achieves 90.43%accuracy(Acc),95.56%Dice similarity coefficient,and 94.66%Intersection over Union with relatively fewer parameters.Finally,UsViT is applied to gear pitting measurement in gear contact fatigue test,and the comparative results indicate that UsViT can improve the Acc of pitting detection.展开更多
Stem diameter is an important parameter in the process of plant growth which can indicate the growth state and moisture content of the plant,its automatic detection is necessary.Traditional devices have many drawbacks...Stem diameter is an important parameter in the process of plant growth which can indicate the growth state and moisture content of the plant,its automatic detection is necessary.Traditional devices have many drawbacks that limit their practical uses in general case.To solve those problems,a stem diameter inspection spherical robot was developed in this study.The particular mechanism of the robot has turned out to be suitable for performing monitoring tasks in greenhouse mainly due to its spherical shape,small size,low weight and traction system that do not produce soil compacting or erosion.The mechanical structure and hardware architecture of the spherical robot were described,the algorithm based on binocular stereo vision was developed to measure the stem diameter of the plant.The effectiveness of the prototype robot was confirmed by field experiments in a tomato greenhouse.The results showed that the machine measurement data was linearly correlated with the manual measurement data with R^(2) of 0.9503.There was no significant difference for each attribute between machine measurement data and manual measurement data(sig>0.05).The results showed that this method was feasible for nondestructive testing of the stem diameter of greenhouse plants.展开更多
基金Supported by National Natural Science Foundation of China (Grant Nos.62033001 and 52175075)Chongqing Municipal Graduate Scientific Research and Innovation Foundation of China (Grant No.CYB21010)。
文摘The judgment of gear failure is based on the pitting area ratio of gear.Traditional gear pitting calculation method mainly rely on manual visual inspection.This method is greatly affected by human factors,and is greatly affected by the working experience,training degree and fatigue degree of the detection personnel,so the detection results may be biased.The non-contact computer vision measurement can carry out non-destructive testing and monitoring under the working condition of the machine,and has high detection accuracy.To improve the measurement accuracy of gear pitting,a novel multi-scale splicing attention U-Net(MSSA U-Net)is explored in this study.An image splicing module is first proposed for concatenating the output feature maps of multiple convolutional layers into a splicing feature map with more semantic information.Then,an attention module is applied to select the key features of the splicing feature map.Given that MSSA U-Net adequately uses multi-scale semantic features,it has better segmentation performance on irregular small objects than U-Net and attention U-Net.On the basis of the designed visual detection platform and MSSA U-Net,a methodology for measuring the area ratio of gear pitting is proposed.With three datasets,experimental results show that MSSA U-Net is superior to existing typical image segmentation methods and can accurately segment different levels of pitting due to its strong segmentation ability.Therefore,the proposed methodology can be effectively applied in measuring the pitting area ratio and determining the level of gear pitting.
基金supported in part by National Natural Science Foundation of China under Grants 62033001 and 52175075.
文摘Although convolutional neural networks have become the mainstream segmentation model,the locality of convolution makes them cannot well learn global and long-range semantic information.To further improve the performance of segmentation models,we propose U-shaped vision Transformer(UsViT),a model based on Transformer and convolution.Specifically,residual Transformer blocks are designed in the encoder of UsViT,which take advantages of residual network and Transformer backbone at the same time.What is more,transpositions in each Transformer layer achieve the information interaction between spatial locations and feature channels,enhancing the capability of feature learning.In the decoder,for enhancing receptive field,different dilation rates are introduced to each convolutional layer.In addition,residual connections are applied to make the information propagation smoother when training the model.We first verify the superiority of UsViT on automatic portrait matting public dataset,which achieves 90.43%accuracy(Acc),95.56%Dice similarity coefficient,and 94.66%Intersection over Union with relatively fewer parameters.Finally,UsViT is applied to gear pitting measurement in gear contact fatigue test,and the comparative results indicate that UsViT can improve the Acc of pitting detection.
基金The authors gratefully thank the financial support provided by the National Key Research and Development Program of China(2018YFD020080709)the Fund for the Returned Overseas Chinese Scholars of Heilongjiang Province(LC2018019)Academic Backbone Foundation of NEAU(17XG01).
文摘Stem diameter is an important parameter in the process of plant growth which can indicate the growth state and moisture content of the plant,its automatic detection is necessary.Traditional devices have many drawbacks that limit their practical uses in general case.To solve those problems,a stem diameter inspection spherical robot was developed in this study.The particular mechanism of the robot has turned out to be suitable for performing monitoring tasks in greenhouse mainly due to its spherical shape,small size,low weight and traction system that do not produce soil compacting or erosion.The mechanical structure and hardware architecture of the spherical robot were described,the algorithm based on binocular stereo vision was developed to measure the stem diameter of the plant.The effectiveness of the prototype robot was confirmed by field experiments in a tomato greenhouse.The results showed that the machine measurement data was linearly correlated with the manual measurement data with R^(2) of 0.9503.There was no significant difference for each attribute between machine measurement data and manual measurement data(sig>0.05).The results showed that this method was feasible for nondestructive testing of the stem diameter of greenhouse plants.
