针对多目标识别过程中点云分类和分割精度不高的问题,提出了一种基于改进Transformer模型的点云分类与分割方法DRPT(Double randomness Point Transformer),该方法在Transformer模型卷积投影层创建新的点嵌入,利用局部邻域的动态处理在...
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针对多目标识别过程中点云分类和分割精度不高的问题,提出了一种基于改进Transformer模型的点云分类与分割方法DRPT(Double randomness Point Transformer),该方法在Transformer模型卷积投影层创建新的点嵌入,利用局部邻域的动态处理在数据特征向量中持续增加全局特征属性,从而提高多目标识别中点云分类和分割的精度。实验中采用了标准基准数据集(ModelNet40、ShapeNet部分分割和SemanticKITTI场景语义分割数据集)以验证模型的性能,实验结果表明:DRPT模型的pIoU值为85.9%,比其他模型平均高出3.5%,有效提高了多目标识别检测时点云分类与分割精度,是对智能网联技术发展的有效支撑。
Automatic target Recognition (ATR) is a prominent step in Maritime Domain Awareness (MDA). These missions are aided by data from multiple sensors like Electro Optic-infrared (EO/IR) cameras, Synthetic Aperture Radar (...
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Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data. In particular, its capability has expan...
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Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data. In particular, its capability has expanded from unmixing data samples and detecting targets at the subpixel scale to finding endmembers, which generally cannot be resolved by multispectral imaging. Accordingly, a wealth of new HSI research has been conducted and reported in the literature in recent years. The aim of this Special Issue "Advances in Hyperspectral Data Exploitation" is to provide a forum for scholars and researchers to publish and share their research ideas and findings to facilitate the utility of hyperspectral imaging in data exploitation and other applications. With this in mind, this Special Issue accepted and published 19 papers in various areas, which can be organized into 9 categories, including I: Hyperspectral Image classification, II: Hyperspectral targetdetection, III: Hyperspectral and Multispectral Fusion, IV: Mid-wave infrared Hyperspectral Imaging, V: Hyperspectral Unmixing, VI: Hyperspectral Sensor Hardware Design, VII: Hyperspectral Reconstruction, VIII: Hyperspectral Visualization, and IX: Applications.
Surveillance applications demand round the clock monitoring of regions in constrained illumination conditions. Thermal infrared cameras which capture the heat emitted by the objects present in the scene appear as a su...
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ISBN:
(数字)9781510635661
ISBN:
(纸本)9781510635661;9781510635654
Surveillance applications demand round the clock monitoring of regions in constrained illumination conditions. Thermal infrared cameras which capture the heat emitted by the objects present in the scene appear as a suitable sensor technology for such applications. However, developing of AI techniques for automatic detection of targets for monitoring applications is challenging due to high variability of targets within a class, variations in pose of targets, widely varying environmental conditions, etc. This paper presents a real-time framework to detect and classify targets in a forest landscape. The system comprises of two main stages: the moving targetdetection and detected targetclassification. For the first stage, Mixture of Gaussians (MoG) background subtraction is used for detection of Region of Interest (ROI) from individual frames of the IR video sequence. For the second stage, a pre-trained Deep Convolutional Neural Network with additional custom layers has been used for the feature extraction and classification. A challenging thermal dataset created by using both experimentally generated thermal infrared images and from publically available FLIR Thermal Dataset. This dataset is used for training and validating the proposed deep learning framework. The model demonstrated a preliminary testing accuracy of 95%. The real-time deployment of the framework is done on embedded platform having an 8-core ARM v8.2 64-bit CPU and 512-core Volta GPU with Tensor Cores. The moving targetdetection and recognition framework achieved a frame rate of approximately 23 fps on this embedded computing platform, making it suitable for deployment in resource constrained environments.
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