Architectural plan generation via pix2pix series algorithms faces dual challenges:the absence of domain-specific evaluation metrics and a lack of systematic insights into the joint impact of training configurations.To...Architectural plan generation via pix2pix series algorithms faces dual challenges:the absence of domain-specific evaluation metrics and a lack of systematic insights into the joint impact of training configurations.To address the limitations of pix2pix-based models adaptation to architectural design,we designed a training regimen involving 12 experiments with varying training set sizes,dataset characteristics,and algorithms.These experiments utilized our self-built,high-quality,large-volume synthetic dataset of architectural-like plans.By saving intermediate models,we obtained 240 generative models for evaluation on a fixed test set.To quantify model performance,we developed a dual-aspect evaluation method that assesses predictions through pixel similarity(principle adherence)and segmentation line continuity(vectorization quality).Analysis revealed algorithm choice and training set size as primary factors,with larger sets enhancing the benefits of high-resolution and enhancedannotation datasets.The optimal model achieved high-quality predictions,demonstrating strict adherence to predefined principles(0.81 similarity)and effective vectorization(0.86 segmentation line continuity).Testing on 7695 samples of varying complexity confirmed the model’s robustness,strong generative capability,and controlled innovation within defined principles,validated through 3D model conversion.This work provides a domain-adapted framework for training and evaluating pix2pix-based architectural generators,bridging generative research and practical applications.展开更多
Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,an...Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.展开更多
The polyp dataset involves the confidentiality of medical records, so it might be difficult to obtain datasets with accurate annotations. This problem can be effectively solved by expanding the polyp data set with alg...The polyp dataset involves the confidentiality of medical records, so it might be difficult to obtain datasets with accurate annotations. This problem can be effectively solved by expanding the polyp data set with algorithms. The traditional polyp dataset expansion scheme usually requires the use of two models or traditional visual methods. These methods are both tedious and difficult to provide new polyp features for training data. Therefore, our research aims to efficiently generate high-quality polyp samples, so as to effectively expand the polyp dataset. In this study, we first added the attention mechanism to the generation model and improved the loss function to reduce the interference caused by reflection in the image generation process. Meanwhile, we used the improved generation model to remove polyps from the original image. In addition, we used masks of different shapes generated by random combinations to generate polyps with more characteristic information. The same generation model was used for the removal and generation of polyps. The generated polyp image has its own annotation, which is conducive to us directly using the expanded data set for training. Finally, we verified the effectiveness of the improved model and the dataset expansion scheme through a series of comparative experiments on the public dataset. The results showed that using the dataset we generate for training can significantly optimize the main performance indicators.展开更多
The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio m...The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio map serves as a crucial tool for describing signal strength distribution within the current electromagnetic environment.However,most existing algorithms rely on sparse measurements of radio strength,disregarding the impact of building information.In this paper,we propose a spectrum cartography(SC)algorithm that eliminates the need for relying on sparse ground-based radio strength measurements by utilizing a satellite network to collect data on buildings and transmitters.Our algorithm leverages Pix2Pix Generative Adversarial Network(GAN)to construct accurate radio maps using transmitter information within real geographical environments.Finally,simulation results demonstrate that our algorithm exhibits superior accuracy compared to previously proposed methods.展开更多
文摘Architectural plan generation via pix2pix series algorithms faces dual challenges:the absence of domain-specific evaluation metrics and a lack of systematic insights into the joint impact of training configurations.To address the limitations of pix2pix-based models adaptation to architectural design,we designed a training regimen involving 12 experiments with varying training set sizes,dataset characteristics,and algorithms.These experiments utilized our self-built,high-quality,large-volume synthetic dataset of architectural-like plans.By saving intermediate models,we obtained 240 generative models for evaluation on a fixed test set.To quantify model performance,we developed a dual-aspect evaluation method that assesses predictions through pixel similarity(principle adherence)and segmentation line continuity(vectorization quality).Analysis revealed algorithm choice and training set size as primary factors,with larger sets enhancing the benefits of high-resolution and enhancedannotation datasets.The optimal model achieved high-quality predictions,demonstrating strict adherence to predefined principles(0.81 similarity)and effective vectorization(0.86 segmentation line continuity).Testing on 7695 samples of varying complexity confirmed the model’s robustness,strong generative capability,and controlled innovation within defined principles,validated through 3D model conversion.This work provides a domain-adapted framework for training and evaluating pix2pix-based architectural generators,bridging generative research and practical applications.
基金supported by the Natural Science Foundation of China,Grant No.62103052.
文摘Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.
基金supported by the Natural Science Foundation Project of Fujian Province,China(Grant Nos.2023J011439 and 2019J01859).
文摘The polyp dataset involves the confidentiality of medical records, so it might be difficult to obtain datasets with accurate annotations. This problem can be effectively solved by expanding the polyp data set with algorithms. The traditional polyp dataset expansion scheme usually requires the use of two models or traditional visual methods. These methods are both tedious and difficult to provide new polyp features for training data. Therefore, our research aims to efficiently generate high-quality polyp samples, so as to effectively expand the polyp dataset. In this study, we first added the attention mechanism to the generation model and improved the loss function to reduce the interference caused by reflection in the image generation process. Meanwhile, we used the improved generation model to remove polyps from the original image. In addition, we used masks of different shapes generated by random combinations to generate polyps with more characteristic information. The same generation model was used for the removal and generation of polyps. The generated polyp image has its own annotation, which is conducive to us directly using the expanded data set for training. Finally, we verified the effectiveness of the improved model and the dataset expansion scheme through a series of comparative experiments on the public dataset. The results showed that using the dataset we generate for training can significantly optimize the main performance indicators.
文摘The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio map serves as a crucial tool for describing signal strength distribution within the current electromagnetic environment.However,most existing algorithms rely on sparse measurements of radio strength,disregarding the impact of building information.In this paper,we propose a spectrum cartography(SC)algorithm that eliminates the need for relying on sparse ground-based radio strength measurements by utilizing a satellite network to collect data on buildings and transmitters.Our algorithm leverages Pix2Pix Generative Adversarial Network(GAN)to construct accurate radio maps using transmitter information within real geographical environments.Finally,simulation results demonstrate that our algorithm exhibits superior accuracy compared to previously proposed methods.
