The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the su...The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.展开更多
1. INTRODUCTION The proposed Three Gorges Project, one of the biggest hydroelectric projects in the world, will dam the middle reaches of the Changjiang (Yangtze) River, the third longest river in the world, and form ...1. INTRODUCTION The proposed Three Gorges Project, one of the biggest hydroelectric projects in the world, will dam the middle reaches of the Changjiang (Yangtze) River, the third longest river in the world, and form a large reservoir. Its impacts on environment have attracted wide attention. Entrusted by National Scientific-Technical Commission, the Chinese Academy of Sciences (CAS) was in charge of a research project on this issuse from 1984 to 1989. Tho use of remote sensing played an important role in the project considering the study area is mountainous and not convenientlv located, which makes it difficult to conduct the research onlv using conventional means.展开更多
Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the...Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.展开更多
Sensing in wireless local area network(WLAN) gains great interests recently. In this paper we focus on the multi-user WLAN sensing problem under the existing 802.11 standards. Multiple stations perform sensing with th...Sensing in wireless local area network(WLAN) gains great interests recently. In this paper we focus on the multi-user WLAN sensing problem under the existing 802.11 standards. Multiple stations perform sensing with the access point and transmit channel state information(CSI)report simultaneously on the basis of uplink-orthogonal frequency division multiple access(OFDMA). Considering the transmission resource consumed in CSI report and the padding wastage in OFDMA based CSI report, we optimize the CSI simplification and uplink resource unit(RU)allocation jointly, aiming to balance the sensing accuracy and padding wastage performances in WLAN sensing. We propose the minimize padding maximize efficiency(MPME) algorithm to solve the problem and evaluate the performance of the proposed algorithm through extensive simulations.展开更多
Real-time wide-area environment sensing is crucial for accessing open-world information streams from nature and human society.As a transformative technique distinct from electrical sensors,distributed optical fiber se...Real-time wide-area environment sensing is crucial for accessing open-world information streams from nature and human society.As a transformative technique distinct from electrical sensors,distributed optical fiber sensing especially for Brillouin scattering-based paradigm has shown superior bandwidth,power,and sensing range.Still,it suffers from insufficient resolution and timeliness to characterize remote dynamic events.Here we develop TABS—a transient acoustic wave-based Brillouin optical time domain analysis sensor,supporting long-range highspatiotemporal-resolution distributed sensing.By designing a functionally synergistic sensor architecture,TABS elaborately leverages wideband and time-weighted energy transformation properties of a transient acousto-optic interaction to breaking through Brillouin-energy-utilization-efficiency bottleneck,enabling enhancements in overall sensing performance.In the experiment,TABS has achieved a 37-cm spatial resolution over a 50-km range with 1 to 2 orders of magnitude improvement in temporal resolution compared to prevailing Brillouin sensing approaches.For the first time,TABS is explored for state imaging of evacuated-tube maglev transportation system as an exemplary application,showcasing its feasibility and flexibility for potential open-world applications and large-scale intelligent perception.展开更多
The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scali...The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.展开更多
基金the Changsha Science and Technology Plan 2004081in part by the Science and Technology Program of Hunan Provincial Department of Transportation 202117in part by the Science and Technology Research and Development Program Project of the China Railway Group Limited 2021-Special-08.
文摘The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.
文摘1. INTRODUCTION The proposed Three Gorges Project, one of the biggest hydroelectric projects in the world, will dam the middle reaches of the Changjiang (Yangtze) River, the third longest river in the world, and form a large reservoir. Its impacts on environment have attracted wide attention. Entrusted by National Scientific-Technical Commission, the Chinese Academy of Sciences (CAS) was in charge of a research project on this issuse from 1984 to 1989. Tho use of remote sensing played an important role in the project considering the study area is mountainous and not convenientlv located, which makes it difficult to conduct the research onlv using conventional means.
基金the Egyptian Ministry of Higher Education and Scientific Research
文摘Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.
基金supported in part by Sichuan Science and Technology Program (Nos. 2022NSFSC0912, 2020YJ0218,2021YFQ056 and 2022YFG0170)Fundamental Research Funds for the Central Universities (Nos. 2682021ZTPY051and 2682021CF019)+2 种基金NSFC (No. 62071393)NSFC High-Speed Rail Joint Foundation (No. U1834210)111 Project 111-2-14。
文摘Sensing in wireless local area network(WLAN) gains great interests recently. In this paper we focus on the multi-user WLAN sensing problem under the existing 802.11 standards. Multiple stations perform sensing with the access point and transmit channel state information(CSI)report simultaneously on the basis of uplink-orthogonal frequency division multiple access(OFDMA). Considering the transmission resource consumed in CSI report and the padding wastage in OFDMA based CSI report, we optimize the CSI simplification and uplink resource unit(RU)allocation jointly, aiming to balance the sensing accuracy and padding wastage performances in WLAN sensing. We propose the minimize padding maximize efficiency(MPME) algorithm to solve the problem and evaluate the performance of the proposed algorithm through extensive simulations.
基金supported in part by National Natural Science Foundation of China(NSFC)under contracts Nos.U23A20376,62431024,61735015,62405153,62205176.
文摘Real-time wide-area environment sensing is crucial for accessing open-world information streams from nature and human society.As a transformative technique distinct from electrical sensors,distributed optical fiber sensing especially for Brillouin scattering-based paradigm has shown superior bandwidth,power,and sensing range.Still,it suffers from insufficient resolution and timeliness to characterize remote dynamic events.Here we develop TABS—a transient acoustic wave-based Brillouin optical time domain analysis sensor,supporting long-range highspatiotemporal-resolution distributed sensing.By designing a functionally synergistic sensor architecture,TABS elaborately leverages wideband and time-weighted energy transformation properties of a transient acousto-optic interaction to breaking through Brillouin-energy-utilization-efficiency bottleneck,enabling enhancements in overall sensing performance.In the experiment,TABS has achieved a 37-cm spatial resolution over a 50-km range with 1 to 2 orders of magnitude improvement in temporal resolution compared to prevailing Brillouin sensing approaches.For the first time,TABS is explored for state imaging of evacuated-tube maglev transportation system as an exemplary application,showcasing its feasibility and flexibility for potential open-world applications and large-scale intelligent perception.
基金supported by the National Natural Science Foundation of China(Grant Nos.91025006,40871186,40730525)National Basic Research Program of China(Grant No.2007CB714402)National High Technology Research and Development Program of China(Grant Nos.2009AA12Z143,2009AA122103)
文摘The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.
