Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation...Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.展开更多
UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience,low cost and convenient maintenance.In marine environmental monitoring,the similarity between...UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience,low cost and convenient maintenance.In marine environmental monitoring,the similarity between objects such as oil spill and sea surface,Spartina alterniflora and algae is high,and the effect of the general segmentation algorithm is poor,which brings new challenges to the segmentation of UAV marine images.Panoramic segmentation can do object detection and semantic segmentation at the same time,which can well solve the polymorphism problem of objects in UAV ocean images.Currently,there are few studies on UAV marine image recognition with panoptic segmentation.In addition,there are no publicly available panoptic segmentation datasets for UAV images.In this work,we collect and annotate UAV images to form a panoptic segmentation UAV dataset named UAV-OUC-SEG and propose a panoptic segmentation method named PanopticUAV.First,to deal with the large intraclass variability in scale,deformable convolution and CBAM attention mechanism are employed in the backbone to obtain more accurate features.Second,due to the complexity and diversity of marine images,boundary masks by the Laplacian operator equation from the ground truth are merged into feature maps to improve boundary segmentation precision.Experiments demonstrate the advantages of PanopticUAV beyond the most other advanced approaches on the UAV-OUC-SEG dataset.展开更多
Towns＇ and cities＇ stories show their multidisciplinary richness and their complexity between professional skills and ways of life and residence of inhabitants by the analysis of the towns＇ changes. Through two pro...Towns＇ and cities＇ stories show their multidisciplinary richness and their complexity between professional skills and ways of life and residence of inhabitants by the analysis of the towns＇ changes. Through two protocols of research, during ten years from 2003 to 2012 and so on, of metropolitan changes in Bordeaux and travels in French and European cities, Christian Sallenave shows that： When everyone can write, tell and recognize his own story of town＇s changes, the cities know the best conditions for their sustainable development; Neither particular, nor general, but both in their specific dialectic; Neither panoptical and sovereign disciplines nor judgmental dopes, but both in their coming and goings and their ＂patriotic＂ property and register. The towns＇ stories are always written and marked in the same time in plural and singular, of arts, sciences and knowledge and pragmatic ways of life and residence.展开更多
In recent years,a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies.The primary focus of computer vision based...In recent years,a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies.The primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs to.So it is required to combine segmentation and detection in a single framework.Recently many successful computer vision methods has been developed to aid scene understanding for a variety of real world application.Scene understanding systems typically involves detection and segmentation of different natural and manmade things.A lot of research has been performed in recent years,mostly with a focus on things(a well-defined objects that has shape,orientations and size)with a less focus on stuff classes(amorphous regions that are unclear and lack a shape,size or other characteristics Stuff region describes many aspects of scene,like type,situation,environment of scene etc.and hence can be very helpful in scene understanding.Existing methods for scene understanding still have to cover a challenging path to cope up with the challenges of computational time,accuracy and robustness for varying level of scene complexity.A robust scene understanding method has to effectively deal with imbalanced distribution of classes,overlapping objects,fuzzy object boundaries and poorly localized objects.The proposed method presents Panoptic Segmentation on Cityscapes Dataset.Mobilenet-V2 is used as a backbone for feature extraction that is pre-trained on ImageNet.MobileNet-V2 with state-of-art encoder-decoder architecture of DeepLabV3+with some customization and optimization is employed Atrous convolution along with Spatial Pyramid Pooling are also utilized in the proposed method to make it more accurate and robust.Very promising and encouraging results have been achieved that indicates the potential of the proposed method for robust scene understanding in a fast and reliable way.展开更多
Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that...Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.展开更多
Accurate panoptic segmentation of 3D point clouds in outdoor scenes is critical for the success of applications such as autonomous driving and robot navigation.Existing methods in this area typically assume that the d...Accurate panoptic segmentation of 3D point clouds in outdoor scenes is critical for the success of applications such as autonomous driving and robot navigation.Existing methods in this area typically assume that the differences between instances are greater than the differences between points belonging to the same instance and use heuristic techniques for segmentation.However,this assumption may not hold in real scenes with occlusion and noise.In addition,most of the previous methods formulate point-wise embedding learning and instance clustering as two decoupled steps for separate optimization,making it a challenging task to learn discriminative embeddings.To address these issues,we introduce a framework for modeling points belonging to the same instance using learnable Gaussian distributions and formulate the point cloud as a Gaussian mixture model.Based on this formulation,we introduce a unified loss function that links the embedding learning and instance clustering in an end-to-end manner.Our framework is generic and can be seamlessly incorporated with existing panoptic segmentation networks.By explicitly modeling intra-instance variance and leveraging end-to-end optimization,our framework improves the discrimination capability of point embeddings with higher accuracy and robustness.Extensive experiments on two large-scale benchmarks demonstrate the effectiveness of the proposed method.展开更多
The global community witnessed vigorous and aggressive campaigns in the past three decades since the advent of HIV and AIDS. Various strategies have been used in this regard in order to advocate safer sex practices am...The global community witnessed vigorous and aggressive campaigns in the past three decades since the advent of HIV and AIDS. Various strategies have been used in this regard in order to advocate safer sex practices among the youth. The article argues that although three decades later, HIV infections are reported to be declining in the regions that were worst hard hit, specifically southern Africa, and there is still a need to promote condom-use among youth aged between 15 and 25. Through text and reception analysis, the article examines discourses of sexual self-responsibility in a purposively selected poster(advocating condom-use) from a host of HIV and AIDS prevention posters and banners advocating HIV and AIDS prevention in 2006-2009, from the University of KwaZulu-Natal, South Africa. Informed by Foucault's notion of the "panoptic gaze" and "techniques of the self", an in-depth textual analysis of the posters is conducted. Norman Fairclough's CDA, augmented by Thompson's ideologies and Kress and van Leeuwen's The Grammar of Visual Design, are used to reveal the language and visual strategies used by the originators of the posters to reveal risk governmentality that may be subsumed in the interplay between the verbal and non-verbal features used in the texts. Furthermore, Hall's reception theory is employed to reveal responses of the students through Focus Group Discussions. The article analyses the discursive self "I" and the second-person deictic pronoun "You" as strategies employed by the campaigns to promote self-surveillance and individual agency. The article argues for continued efforts in condom promotion to reduce HIV infections and while doing so, for the inclusion of youth in designing prevention messages.展开更多
文摘Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.
基金This work was partially supported by the National Key Research and Development Program of China under Grant No.2018AAA0100400the Natural Science Foundation of Shandong Province under Grants Nos.ZR2020MF131 and ZR2021ZD19the Science and Technology Program of Qingdao under Grant No.21-1-4-ny-19-nsh.
文摘UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience,low cost and convenient maintenance.In marine environmental monitoring,the similarity between objects such as oil spill and sea surface,Spartina alterniflora and algae is high,and the effect of the general segmentation algorithm is poor,which brings new challenges to the segmentation of UAV marine images.Panoramic segmentation can do object detection and semantic segmentation at the same time,which can well solve the polymorphism problem of objects in UAV ocean images.Currently,there are few studies on UAV marine image recognition with panoptic segmentation.In addition,there are no publicly available panoptic segmentation datasets for UAV images.In this work,we collect and annotate UAV images to form a panoptic segmentation UAV dataset named UAV-OUC-SEG and propose a panoptic segmentation method named PanopticUAV.First,to deal with the large intraclass variability in scale,deformable convolution and CBAM attention mechanism are employed in the backbone to obtain more accurate features.Second,due to the complexity and diversity of marine images,boundary masks by the Laplacian operator equation from the ground truth are merged into feature maps to improve boundary segmentation precision.Experiments demonstrate the advantages of PanopticUAV beyond the most other advanced approaches on the UAV-OUC-SEG dataset.
文摘Towns＇ and cities＇ stories show their multidisciplinary richness and their complexity between professional skills and ways of life and residence of inhabitants by the analysis of the towns＇ changes. Through two protocols of research, during ten years from 2003 to 2012 and so on, of metropolitan changes in Bordeaux and travels in French and European cities, Christian Sallenave shows that： When everyone can write, tell and recognize his own story of town＇s changes, the cities know the best conditions for their sustainable development; Neither particular, nor general, but both in their specific dialectic; Neither panoptical and sovereign disciplines nor judgmental dopes, but both in their coming and goings and their ＂patriotic＂ property and register. The towns＇ stories are always written and marked in the same time in plural and singular, of arts, sciences and knowledge and pragmatic ways of life and residence.
文摘In recent years,a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies.The primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs to.So it is required to combine segmentation and detection in a single framework.Recently many successful computer vision methods has been developed to aid scene understanding for a variety of real world application.Scene understanding systems typically involves detection and segmentation of different natural and manmade things.A lot of research has been performed in recent years,mostly with a focus on things(a well-defined objects that has shape,orientations and size)with a less focus on stuff classes(amorphous regions that are unclear and lack a shape,size or other characteristics Stuff region describes many aspects of scene,like type,situation,environment of scene etc.and hence can be very helpful in scene understanding.Existing methods for scene understanding still have to cover a challenging path to cope up with the challenges of computational time,accuracy and robustness for varying level of scene complexity.A robust scene understanding method has to effectively deal with imbalanced distribution of classes,overlapping objects,fuzzy object boundaries and poorly localized objects.The proposed method presents Panoptic Segmentation on Cityscapes Dataset.Mobilenet-V2 is used as a backbone for feature extraction that is pre-trained on ImageNet.MobileNet-V2 with state-of-art encoder-decoder architecture of DeepLabV3+with some customization and optimization is employed Atrous convolution along with Spatial Pyramid Pooling are also utilized in the proposed method to make it more accurate and robust.Very promising and encouraging results have been achieved that indicates the potential of the proposed method for robust scene understanding in a fast and reliable way.
文摘Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.
文摘Accurate panoptic segmentation of 3D point clouds in outdoor scenes is critical for the success of applications such as autonomous driving and robot navigation.Existing methods in this area typically assume that the differences between instances are greater than the differences between points belonging to the same instance and use heuristic techniques for segmentation.However,this assumption may not hold in real scenes with occlusion and noise.In addition,most of the previous methods formulate point-wise embedding learning and instance clustering as two decoupled steps for separate optimization,making it a challenging task to learn discriminative embeddings.To address these issues,we introduce a framework for modeling points belonging to the same instance using learnable Gaussian distributions and formulate the point cloud as a Gaussian mixture model.Based on this formulation,we introduce a unified loss function that links the embedding learning and instance clustering in an end-to-end manner.Our framework is generic and can be seamlessly incorporated with existing panoptic segmentation networks.By explicitly modeling intra-instance variance and leveraging end-to-end optimization,our framework improves the discrimination capability of point embeddings with higher accuracy and robustness.Extensive experiments on two large-scale benchmarks demonstrate the effectiveness of the proposed method.
文摘The global community witnessed vigorous and aggressive campaigns in the past three decades since the advent of HIV and AIDS. Various strategies have been used in this regard in order to advocate safer sex practices among the youth. The article argues that although three decades later, HIV infections are reported to be declining in the regions that were worst hard hit, specifically southern Africa, and there is still a need to promote condom-use among youth aged between 15 and 25. Through text and reception analysis, the article examines discourses of sexual self-responsibility in a purposively selected poster(advocating condom-use) from a host of HIV and AIDS prevention posters and banners advocating HIV and AIDS prevention in 2006-2009, from the University of KwaZulu-Natal, South Africa. Informed by Foucault's notion of the "panoptic gaze" and "techniques of the self", an in-depth textual analysis of the posters is conducted. Norman Fairclough's CDA, augmented by Thompson's ideologies and Kress and van Leeuwen's The Grammar of Visual Design, are used to reveal the language and visual strategies used by the originators of the posters to reveal risk governmentality that may be subsumed in the interplay between the verbal and non-verbal features used in the texts. Furthermore, Hall's reception theory is employed to reveal responses of the students through Focus Group Discussions. The article analyses the discursive self "I" and the second-person deictic pronoun "You" as strategies employed by the campaigns to promote self-surveillance and individual agency. The article argues for continued efforts in condom promotion to reduce HIV infections and while doing so, for the inclusion of youth in designing prevention messages.
