In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in term...In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations.A virtual dataset named Parallel Eye is built, which can be used for several computer vision tasks. Then, by training the DPM(Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining Parallel Eye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.展开更多
Space object recognition plays an important role in spatial exploitation and surveillance, followed by two main problems： lacking of data and drastic changes in viewpoints. In this article, firstly, we build a three-...Space object recognition plays an important role in spatial exploitation and surveillance, followed by two main problems： lacking of data and drastic changes in viewpoints. In this article, firstly, we build a three-dimensional （3D） satellites dataset named BUAA Satellite Image Dataset （BUAA-SID 1.0） to supply data for 3D space object research. Then, based on the dataset, we propose to recognize full-viewpoint 3D space objects based on kernel locality preserving projections （KLPP）. To obtain more accurate and separable description of the objects, firstly, we build feature vectors employing moment invariants, Fourier descriptors, region covariance and histogram of oriented gradients. Then, we map the features into kernel space followed by dimensionality reduction using KLPP to obtain the submanifold of the features. At last, k-nearest neighbor （kNN） is used to accomplish the classification. Experimental results show that the proposed approach is more appropriate for space object recognition mainly considering changes of viewpoints. Encouraging recognition rate could be obtained based on images in BUAA-SID 1.0, and the highest recognition result could achieve 95.87%.展开更多
【目的】矿产资源是人类生存和经济发展的重要物质基础,开展矿山监测、建立矿山监测模型对矿产资源的高效开发和矿区环境保护具有重要意义。针对露天矿区背景复杂、目标尺度多样且小目标聚集的特点,本研究旨在构建兼顾监测精度与效率的...【目的】矿产资源是人类生存和经济发展的重要物质基础,开展矿山监测、建立矿山监测模型对矿产资源的高效开发和矿区环境保护具有重要意义。针对露天矿区背景复杂、目标尺度多样且小目标聚集的特点,本研究旨在构建兼顾监测精度与效率的轻量化模型,以提升矿区目标地物监测的准确性和效率。【方法】现有遥感数据集存在的样本单一、地域局限等问题,因此本文基于0.9 m天地图与1.8 m谷歌影像构建了不同气候背景、大范围和多种地物的六大露天煤矿基地OMTSFD(Open-pit Mine Typical Surface Features Dataset)数据集,提出改进的YOLO11-DAE算法进行模型训练与验证。首先,在骨干网络和特征金字塔中引入C3K2-DBB模块以增强多尺度特征捕获能力;其次,采用ADown模块替换网络下采样卷积,增强了模块对不同特征的表征能力,减少了低对比度场景的细节丢失;最后,采用E_Detect高效检测头降低模型复杂度和参数量,实现模型轻量化。【结果】实验表明,YOLO11-DAE的每秒帧数(Frames Per Second,FPS)为528.100,模型推理速度较快,精确率(Precision,P)、召回率(Recall,R)、综合评价指标(F1-Score,F1)、平均精度均值(Mean Average Precision,mAP)分别达到0.932、0.894、0.913和0.950,显著优于YOLOv5n、YOLOv8n和YOLOv10n算法,相较于YOLOv11n各项指标分别提高7.600%、10.000%、8.800%、8.000%。【结论】YOLO11-DAE算法能够满足矿区实时监测,并适用于多尺度、多背景等复杂场景的目标识别,实现了高精度、低漏检率的监测目标,达到了模型可应用性与实时性的平衡。展开更多
基金supported by the National Natural Science Foundation of China(61533019,71232006)
文摘In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations.A virtual dataset named Parallel Eye is built, which can be used for several computer vision tasks. Then, by training the DPM(Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining Parallel Eye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.
基金National Natural Science Foundation of China (60776793,60802043)National Basic Research Program of China (2010CB327900)
文摘Space object recognition plays an important role in spatial exploitation and surveillance, followed by two main problems： lacking of data and drastic changes in viewpoints. In this article, firstly, we build a three-dimensional （3D） satellites dataset named BUAA Satellite Image Dataset （BUAA-SID 1.0） to supply data for 3D space object research. Then, based on the dataset, we propose to recognize full-viewpoint 3D space objects based on kernel locality preserving projections （KLPP）. To obtain more accurate and separable description of the objects, firstly, we build feature vectors employing moment invariants, Fourier descriptors, region covariance and histogram of oriented gradients. Then, we map the features into kernel space followed by dimensionality reduction using KLPP to obtain the submanifold of the features. At last, k-nearest neighbor （kNN） is used to accomplish the classification. Experimental results show that the proposed approach is more appropriate for space object recognition mainly considering changes of viewpoints. Encouraging recognition rate could be obtained based on images in BUAA-SID 1.0, and the highest recognition result could achieve 95.87%.
文摘【目的】矿产资源是人类生存和经济发展的重要物质基础,开展矿山监测、建立矿山监测模型对矿产资源的高效开发和矿区环境保护具有重要意义。针对露天矿区背景复杂、目标尺度多样且小目标聚集的特点,本研究旨在构建兼顾监测精度与效率的轻量化模型,以提升矿区目标地物监测的准确性和效率。【方法】现有遥感数据集存在的样本单一、地域局限等问题,因此本文基于0.9 m天地图与1.8 m谷歌影像构建了不同气候背景、大范围和多种地物的六大露天煤矿基地OMTSFD(Open-pit Mine Typical Surface Features Dataset)数据集,提出改进的YOLO11-DAE算法进行模型训练与验证。首先,在骨干网络和特征金字塔中引入C3K2-DBB模块以增强多尺度特征捕获能力;其次,采用ADown模块替换网络下采样卷积,增强了模块对不同特征的表征能力,减少了低对比度场景的细节丢失;最后,采用E_Detect高效检测头降低模型复杂度和参数量,实现模型轻量化。【结果】实验表明,YOLO11-DAE的每秒帧数(Frames Per Second,FPS)为528.100,模型推理速度较快,精确率(Precision,P)、召回率(Recall,R)、综合评价指标(F1-Score,F1)、平均精度均值(Mean Average Precision,mAP)分别达到0.932、0.894、0.913和0.950,显著优于YOLOv5n、YOLOv8n和YOLOv10n算法,相较于YOLOv11n各项指标分别提高7.600%、10.000%、8.800%、8.000%。【结论】YOLO11-DAE算法能够满足矿区实时监测,并适用于多尺度、多背景等复杂场景的目标识别,实现了高精度、低漏检率的监测目标,达到了模型可应用性与实时性的平衡。
