Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete v...Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.展开更多
Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variati...Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.展开更多
Rain streaks in an image appear in different sizes and orientations,resulting in severe blurring and visual quality degradation.Previous CNNbased algorithms have achieved encouraging deraining results although there a...Rain streaks in an image appear in different sizes and orientations,resulting in severe blurring and visual quality degradation.Previous CNNbased algorithms have achieved encouraging deraining results although there are certain limitations in the description of rain streaks and the restoration of scene structures in different environments.In this paper,we propose an efficient multi-scale enhancement and aggregation network(MEAN)to solve the single-image deraining problem.Considering the importance of large receptive fields and multi-scale features,we introduce a multi-scale enhanced unit(MEU)to capture longrange dependencies and exploit features at different scales to depict rain.Simultaneously,an attentive aggregation unit(AAU)is designed to utilize the informative features in spatial and channel dimensions,thereby aggregating effective information to eliminate redundant features for rich scenario details.To improve the deraining performance of the encoder–decoder network,we utilized an AAU to filter the information in the encoder network and concatenated the useful features to the decoder network,which is conducive to predicting high-quality clean images.Experimental results on synthetic datasets and real-world samples show that the proposed method achieves a significant deraining performance compared to state-of-the-art approaches.展开更多
Many architectural research studies have focused on creating new materials to reduce the exploitation of non-renewable natural resources,achieve sustainable development,and reduce carbon emissions.Desert sand(DS)has a...Many architectural research studies have focused on creating new materials to reduce the exploitation of non-renewable natural resources,achieve sustainable development,and reduce carbon emissions.Desert sand(DS)has attracted interest from researchers who have conducted numerous experimental investigations as a possible replacement for river sand.The idea of utilising DS in place of natural fine aggregates in construction has been demonstrated in the literature.However,to analyse and gain confidence in using DS in concrete,a thorough study of its various properties is needed.Therefore,this study addresses the morphological,chemical,and physical characteristics of DS from multiple perspectives.This review presents a study on the durability of desert sand concrete(DSC)and the use of DS cement-based products,and highlights investigations on the design of mix proportions and fresh and hardened properties of DSC.Research issues are emerging around the use of DS in engineered cementitious composites(ECC)materials and the investigation of desert sand powder(DSP)as mineral admixtures.Many issues need to be resolved quickly,which is crucial for the use of DS.In summary,research on DS is still in its early stages,and no systematic research results have been obtained at present.This review makes several recommendations and attempts to explain why DS will likely be widely used as a building material in the future.展开更多
Before providing services to the user, user preference considerations are the key conditions to achieve the self-adaptive decision-making about service selection and composition process, which is the flexible concerne...Before providing services to the user, user preference considerations are the key conditions to achieve the self-adaptive decision-making about service selection and composition process, which is the flexible concerned aspect provided by massive cloud computing environment data. Meanwhile, during the whole services’ providing process, achieving the capturing and forming of service aggregation units’ topology logic, building the context environment’s process-aware of service composition, ensuring the trust and adaptation among service aggregation units, which are the important reasons to express timely requirement preference. This paper designs SCP-Trust Reasoning strategy about the integration of user preference and trust, with process algebra, it is to achieve the context process-aware logic for service composition process, in order to improve the autonomous optimization and evolution of service implementation system.展开更多
Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological enviro...Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water cycle.However,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of lakes.Therefore,it is a great challenge to completely and effectively extract plateau lakes.In this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake regions.It consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes.The MPPM module aggregated the contextual information of various lakes globally.We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity.Finally,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.展开更多
基金the National Natural Science Foundation of China(No.62266025)。
文摘Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.
基金the Key Research and Development Program of Hainan Province(Grant Nos.ZDYF2023GXJS163,ZDYF2024GXJS014)National Natural Science Foundation of China(NSFC)(Grant Nos.62162022,62162024)+2 种基金the Major Science and Technology Project of Hainan Province(Grant No.ZDKJ2020012)Hainan Provincial Natural Science Foundation of China(Grant No.620MS021)Youth Foundation Project of Hainan Natural Science Foundation(621QN211).
文摘Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.
基金supported by the National Natural Science Foundation of China(No.61972227)the Natural Science Foundation of Shandong Province(No.ZR201808160102)+4 种基金Shandong Provincial Natural Science Foundation Key Project(No.ZR2020KF015)the Key Research and Development Project of Shandong Province(No.2019GSF109112)the Science and Technology Plan for Young Talents in Colleges and Universities of Shandong Province(No.2020KJN007)the Scientific Research Studio in Colleges and Universities of Ji’nan City(No.2021GXRC092)the Science and Technology Research Program for Colleges and Universities in Shandong Province(No.KJ2018BZN029).
文摘Rain streaks in an image appear in different sizes and orientations,resulting in severe blurring and visual quality degradation.Previous CNNbased algorithms have achieved encouraging deraining results although there are certain limitations in the description of rain streaks and the restoration of scene structures in different environments.In this paper,we propose an efficient multi-scale enhancement and aggregation network(MEAN)to solve the single-image deraining problem.Considering the importance of large receptive fields and multi-scale features,we introduce a multi-scale enhanced unit(MEU)to capture longrange dependencies and exploit features at different scales to depict rain.Simultaneously,an attentive aggregation unit(AAU)is designed to utilize the informative features in spatial and channel dimensions,thereby aggregating effective information to eliminate redundant features for rich scenario details.To improve the deraining performance of the encoder–decoder network,we utilized an AAU to filter the information in the encoder network and concatenated the useful features to the decoder network,which is conducive to predicting high-quality clean images.Experimental results on synthetic datasets and real-world samples show that the proposed method achieves a significant deraining performance compared to state-of-the-art approaches.
文摘Many architectural research studies have focused on creating new materials to reduce the exploitation of non-renewable natural resources,achieve sustainable development,and reduce carbon emissions.Desert sand(DS)has attracted interest from researchers who have conducted numerous experimental investigations as a possible replacement for river sand.The idea of utilising DS in place of natural fine aggregates in construction has been demonstrated in the literature.However,to analyse and gain confidence in using DS in concrete,a thorough study of its various properties is needed.Therefore,this study addresses the morphological,chemical,and physical characteristics of DS from multiple perspectives.This review presents a study on the durability of desert sand concrete(DSC)and the use of DS cement-based products,and highlights investigations on the design of mix proportions and fresh and hardened properties of DSC.Research issues are emerging around the use of DS in engineered cementitious composites(ECC)materials and the investigation of desert sand powder(DSP)as mineral admixtures.Many issues need to be resolved quickly,which is crucial for the use of DS.In summary,research on DS is still in its early stages,and no systematic research results have been obtained at present.This review makes several recommendations and attempts to explain why DS will likely be widely used as a building material in the future.
文摘Before providing services to the user, user preference considerations are the key conditions to achieve the self-adaptive decision-making about service selection and composition process, which is the flexible concerned aspect provided by massive cloud computing environment data. Meanwhile, during the whole services’ providing process, achieving the capturing and forming of service aggregation units’ topology logic, building the context environment’s process-aware of service composition, ensuring the trust and adaptation among service aggregation units, which are the important reasons to express timely requirement preference. This paper designs SCP-Trust Reasoning strategy about the integration of user preference and trust, with process algebra, it is to achieve the context process-aware logic for service composition process, in order to improve the autonomous optimization and evolution of service implementation system.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program under Grant 2019QZKK0106the Science and Technology Major Project of Henan Province under Grant 201400210900.
文摘Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water cycle.However,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of lakes.Therefore,it is a great challenge to completely and effectively extract plateau lakes.In this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake regions.It consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes.The MPPM module aggregated the contextual information of various lakes globally.We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity.Finally,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.
