Combined with the printing application,an image registration method based on the multi-resolution morphology contour detection was proposed.First,a direction based multi-resolution gray morphology in the scheme was pr...Combined with the printing application,an image registration method based on the multi-resolution morphology contour detection was proposed.First,a direction based multi-resolution gray morphology in the scheme was proposed to realize the contour extraction.Then,based on the contour features,the subspace image registration was proposed to deal with issues of the computing complexity appeared in the traditional image registration methods.The proposed image registration was efficiently applied in the defect inspection of printing images.展开更多
Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each year.For this purpose,a proper condition...Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each year.For this purpose,a proper condition monitoring system is essential to avoid accidents and heavy losses.Generally,the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment.Therefore,in this paper,we present the development of a novel embedded system prototype for condition monitoring of railway track.The proposed prototype system works in real-time by acquiring railway track surface images and performing two tasks a)detect deformation(i.e.,faults)like squats,shelling,and spalling using the contour feature algorithm and b)the vibration signature on that faulty spot by synchronizing acceleration and image data.A new illumination scheme is also proposed to avoid the sunlight reflection that badly affects the image acquisition process.The contour detection algorithm is applied here to detect the uneven shapes and discontinuities in the geometrical structure of the railway track surface,which ultimately detects unhealthy regions.It works by converting Red,Green,and Blue(RGB)images into binary images,which distinguishes the unhealthy regions by making them white color while the healthy regions in black color.We have used the multiprocessing technique to overcome the massive processing and memory issues.This embedded system is developed on Raspberry Pi by interfacing a vision camera,an accelerometer,a proximity sensor,and a Global Positioning System(GPS)sensors(i.e.,multi-sensors).The developed embedded system prototype is tested in real-time onsite by installing it on a Railway Inspection Trolley(RIT),which runs at an average speed of 15 km/h.The functional verification of the proposed system is done successfully by detecting and recording the various railway track surface faults.An unhealthy frame’s onsite detection processing time was recorded at approximately 25.6ms.The proposed system can synchronize the acceleration data on specific railway track deformation.The proposed novel embedded system may be beneficial for detecting faults to overcome the conventional manual railway track condition monitoring,which is still being practiced in various developing or underdeveloped countries.展开更多
Contour detection has a rich history in multiplefields such as geography,engineering,and earth science.The predominant approach is based on piecewise planar tessellation and now being challenged concerning the extract...Contour detection has a rich history in multiplefields such as geography,engineering,and earth science.The predominant approach is based on piecewise planar tessellation and now being challenged concerning the extraction of contour objects for non-linear elevation functions,particularly with respect to bicubic spline functions.A storage-efficient method was developed in previous research,but the detection of the complete set of contour objects is yet to be realized.Although intractable,theoretical underpinnings pertinent to curvature resulted in an approach to realize the complete detection of objects.Given a digital elevation model dataset,in this study,a bicubic spline surface function wasfirst determined.Thereafter,candidate initial points on the edges across the region of interest were identified,and the recursive disaggregation of rectangles was repeated if the non-existence of a solution could not be assured.A developed tracking method was then applied.During advancement,other initial points on the same contour curve were identified and eliminated to circumvent duplicate detection.The completeness of the outlets provides analytical tools for elevation and other geographical assessments.Demonstrative experiments included the development of a three-dimensional contour-based network and slope assessments.The latter application transforms the slope analysis type from raster-based to vector-based.Highlights.Detection of a complete set of contour objects amenable to bicubic spline surfaces..Small closure inside a single patch is detectable if size exceeds the standard..Curvature&tolerances central to step length adjustment and tangent angle determination..Redundant initial points are identified and eliminated during the tracking process..Various potential applications in addition to geographical elevations.展开更多
Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers,managers,and policymakers in terms of better managing and utilizing their agricultural resources.Due...Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers,managers,and policymakers in terms of better managing and utilizing their agricultural resources.Due to their small size,irregular shape,and the use of mixed-cropping techniques,the farm fields of smallholder can be difficult to delineate automatically.In recent years,numerous studies on field contour extraction using a deep Convolutional Neural Network(CNN)have been proposed.However,there is a relative shortage of labeled data for filed boundaries,thus affecting the training effect of CNN.Traditional methods mostly use image flipping,and random rotation for data augmentation.In this paper,we propose to apply Generative Adversarial Network(GAN)for the data augmentation of farm fields label to increase the diversity of samples.Specifically,we propose an automated method featured by Fully Convolutional Neural networks(FCN)in combination with GAN to improve the delineation accuracy of smallholder farms from Very High Resolution(VHR)images.We first investigate four State-Of-The-Art(SOTA)FCN architectures,i.e.,U-Net,PSPNet,SegNet and OCRNet,to find the optimal architecture in the contour detection task of smallholder farm fields.Second,we apply the identified optimal FCN architecture in combination with Contour GAN and pixel2pixel GAN to improve the accuracy of contour detection.We test our method on the study area in the Sudano-Sahelian savanna region of northern Nigeria.The best combination achieved F1 scores of 0.686 on Test Set 1(TS1),0.684 on Test Set 2(TS2),and 0.691 on Test Set 3(TS3).Results indicate that our architecture adapts to a variety of advanced networks and proves its effectiveness in this task.The conceptual,theoretical,and experimental knowledge from this study is expected to seed many GAN-based farm delineation methods in the future.展开更多
The shearing line is the key to improve the quality and efficiency of heavy plates.A model of contour recognition and intelligent shearing strategy for the heavy plate was proposed.Firstly,multi-array binocular vision...The shearing line is the key to improve the quality and efficiency of heavy plates.A model of contour recognition and intelligent shearing strategy for the heavy plate was proposed.Firstly,multi-array binocular vision linear cameras were used to complete the image acquisition.Secondly,the total length of the steel plate after cooling was predicted by back propagation neural network algorithm according to the contour data.Finally,using the scanning line and a new camber description method,the shearing strategy including head/tail irregular shape length and rough dividing strategy was calculated.The practical application shows that the model and strategy can effectively solve the problems existing in the shearing process and can effectively improve the yield of steel plates.The maximum error of detection width,length,camber,and the length of the irregular deformation area at the head/tail of the plate are all less than 5 mm.The correlation coefficient of the length prediction model based on the back propagation neural network is very high.The reverse ratio result of edge cutting failure using the proposed rough dividing strategy is 1/401=0.2%,which is 2%higher than that by human.展开更多
基金Funded by the National Natural Science Foundation of China(General Program,Key Program,Major Research Plan) (Grant No.60474021)China Postdoctoral Science Foundation (Grant No.20100471180)the Freedom Explore Program of Central South University (Grant No. 2012QNZT017)
文摘Combined with the printing application,an image registration method based on the multi-resolution morphology contour detection was proposed.First,a direction based multi-resolution gray morphology in the scheme was proposed to realize the contour extraction.Then,based on the contour features,the subspace image registration was proposed to deal with issues of the computing complexity appeared in the traditional image registration methods.The proposed image registration was efficiently applied in the defect inspection of printing images.
基金supported by the NCRA project of the Higher Education Commission Pakistan.
文摘Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each year.For this purpose,a proper condition monitoring system is essential to avoid accidents and heavy losses.Generally,the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment.Therefore,in this paper,we present the development of a novel embedded system prototype for condition monitoring of railway track.The proposed prototype system works in real-time by acquiring railway track surface images and performing two tasks a)detect deformation(i.e.,faults)like squats,shelling,and spalling using the contour feature algorithm and b)the vibration signature on that faulty spot by synchronizing acceleration and image data.A new illumination scheme is also proposed to avoid the sunlight reflection that badly affects the image acquisition process.The contour detection algorithm is applied here to detect the uneven shapes and discontinuities in the geometrical structure of the railway track surface,which ultimately detects unhealthy regions.It works by converting Red,Green,and Blue(RGB)images into binary images,which distinguishes the unhealthy regions by making them white color while the healthy regions in black color.We have used the multiprocessing technique to overcome the massive processing and memory issues.This embedded system is developed on Raspberry Pi by interfacing a vision camera,an accelerometer,a proximity sensor,and a Global Positioning System(GPS)sensors(i.e.,multi-sensors).The developed embedded system prototype is tested in real-time onsite by installing it on a Railway Inspection Trolley(RIT),which runs at an average speed of 15 km/h.The functional verification of the proposed system is done successfully by detecting and recording the various railway track surface faults.An unhealthy frame’s onsite detection processing time was recorded at approximately 25.6ms.The proposed system can synchronize the acceleration data on specific railway track deformation.The proposed novel embedded system may be beneficial for detecting faults to overcome the conventional manual railway track condition monitoring,which is still being practiced in various developing or underdeveloped countries.
基金supported by Japan Society for the Promotion of Science[grant number 21 K01021].
文摘Contour detection has a rich history in multiplefields such as geography,engineering,and earth science.The predominant approach is based on piecewise planar tessellation and now being challenged concerning the extraction of contour objects for non-linear elevation functions,particularly with respect to bicubic spline functions.A storage-efficient method was developed in previous research,but the detection of the complete set of contour objects is yet to be realized.Although intractable,theoretical underpinnings pertinent to curvature resulted in an approach to realize the complete detection of objects.Given a digital elevation model dataset,in this study,a bicubic spline surface function wasfirst determined.Thereafter,candidate initial points on the edges across the region of interest were identified,and the recursive disaggregation of rectangles was repeated if the non-existence of a solution could not be assured.A developed tracking method was then applied.During advancement,other initial points on the same contour curve were identified and eliminated to circumvent duplicate detection.The completeness of the outlets provides analytical tools for elevation and other geographical assessments.Demonstrative experiments included the development of a three-dimensional contour-based network and slope assessments.The latter application transforms the slope analysis type from raster-based to vector-based.Highlights.Detection of a complete set of contour objects amenable to bicubic spline surfaces..Small closure inside a single patch is detectable if size exceeds the standard..Curvature&tolerances central to step length adjustment and tangent angle determination..Redundant initial points are identified and eliminated during the tracking process..Various potential applications in addition to geographical elevations.
基金Foundation of Anhui Province Key Laboratory of Physical Geographic Environment(No.2022PGE012)
文摘Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers,managers,and policymakers in terms of better managing and utilizing their agricultural resources.Due to their small size,irregular shape,and the use of mixed-cropping techniques,the farm fields of smallholder can be difficult to delineate automatically.In recent years,numerous studies on field contour extraction using a deep Convolutional Neural Network(CNN)have been proposed.However,there is a relative shortage of labeled data for filed boundaries,thus affecting the training effect of CNN.Traditional methods mostly use image flipping,and random rotation for data augmentation.In this paper,we propose to apply Generative Adversarial Network(GAN)for the data augmentation of farm fields label to increase the diversity of samples.Specifically,we propose an automated method featured by Fully Convolutional Neural networks(FCN)in combination with GAN to improve the delineation accuracy of smallholder farms from Very High Resolution(VHR)images.We first investigate four State-Of-The-Art(SOTA)FCN architectures,i.e.,U-Net,PSPNet,SegNet and OCRNet,to find the optimal architecture in the contour detection task of smallholder farm fields.Second,we apply the identified optimal FCN architecture in combination with Contour GAN and pixel2pixel GAN to improve the accuracy of contour detection.We test our method on the study area in the Sudano-Sahelian savanna region of northern Nigeria.The best combination achieved F1 scores of 0.686 on Test Set 1(TS1),0.684 on Test Set 2(TS2),and 0.691 on Test Set 3(TS3).Results indicate that our architecture adapts to a variety of advanced networks and proves its effectiveness in this task.The conceptual,theoretical,and experimental knowledge from this study is expected to seed many GAN-based farm delineation methods in the future.
基金The paper was prepared under the support of the Natural Science Foundation of Liaoning Province(Grant No.2022-MS-277)This research was also financially supported by the Youth Project of Foundation of Liaoning Province Education Administration(Grant No.lnqn202016).
文摘The shearing line is the key to improve the quality and efficiency of heavy plates.A model of contour recognition and intelligent shearing strategy for the heavy plate was proposed.Firstly,multi-array binocular vision linear cameras were used to complete the image acquisition.Secondly,the total length of the steel plate after cooling was predicted by back propagation neural network algorithm according to the contour data.Finally,using the scanning line and a new camber description method,the shearing strategy including head/tail irregular shape length and rough dividing strategy was calculated.The practical application shows that the model and strategy can effectively solve the problems existing in the shearing process and can effectively improve the yield of steel plates.The maximum error of detection width,length,camber,and the length of the irregular deformation area at the head/tail of the plate are all less than 5 mm.The correlation coefficient of the length prediction model based on the back propagation neural network is very high.The reverse ratio result of edge cutting failure using the proposed rough dividing strategy is 1/401=0.2%,which is 2%higher than that by human.