为了使用各种类型的数据理解现实世界,人工智能(AI)是当今最常用的技术。在分析数据中找到模式的同时表示主要任务。这是通过提取代表性特征步骤来执行的,该步骤是使用统计算法或使用某些特定过滤器进行的。但是,从大规模数据中选择有用的功能代表了至关重要的挑战。现在,随着卷积神经网络(CNN)的发展,功能提取操作变得更加自动和更容易。 CNN允许处理大规模的数据,并涵盖特定任务的不同方案。对于计算机视觉任务,卷积网络也用于为深度学习模型的其他部分提取功能。选择合适的网络用于特征提取或DL模型的其他部分不是随机工作。因此,这种模型的实现可能与目标任务以及其计算复杂性有关。已经提出了许多网络,并成为任何AI任务中任何DL模型的著名网络。这些网络被利用用于特征提取或在任何名为骨架的DL模型的开头。骨干是以前在许多其他任务中训练并证明其有效性的已知网络。在本文中,现有骨干的概述,例如详细说明给出了VGG,Resnets,Densenet等。此外,通过对所使用的骨干进行审查,讨论了几个计算机视觉任务。此外,还基于每个任务的骨干,还提供了性能的比较。
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视频分析的图像分割在不同的研究领域起着重要作用,例如智能城市,医疗保健,计算机视觉和地球科学以及遥感应用。在这方面,最近致力于发展新的细分策略;最新的杰出成就之一是Panoptic细分。后者是由语义和实例分割的融合引起的。明确地,目前正在研究Panoptic细分,以帮助获得更多对视频监控,人群计数,自主驾驶,医学图像分析的图像场景的更细致的知识,以及一般对场景更深入的了解。为此,我们介绍了本文的首次全面审查现有的Panoptic分段方法,以获得作者的知识。因此,基于所采用的算法,应用场景和主要目标的性质,执行现有的Panoptic技术的明确定义分类。此外,讨论了使用伪标签注释新数据集的Panoptic分割。继续前进,进行消融研究,以了解不同观点的Panoptic方法。此外,讨论了适合于Panoptic分割的评估度量,并提供了现有解决方案性能的比较,以告知最先进的并识别其局限性和优势。最后,目前对主题技术面临的挑战和吸引不久的将来吸引相当兴趣的未来趋势,可以成为即将到来的研究研究的起点。提供代码的文件可用于:https://github.com/elharroussomar/awesome-panoptic-egation
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Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.
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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.
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2019年12月,一个名为Covid-19的新型病毒导致了迄今为止的巨大因果关系。与新的冠状病毒的战斗在西班牙语流感后令人振奋和恐怖。虽然前线医生和医学研究人员在控制高度典型病毒的传播方面取得了重大进展,但技术也证明了在战斗中的重要性。此外,许多医疗应用中已采用人工智能,以诊断许多疾病,甚至陷入困境的经验丰富的医生。因此,本调查纸探讨了提议的方法,可以提前援助医生和研究人员,廉价的疾病诊断方法。大多数发展中国家难以使用传统方式进行测试,但机器和深度学习可以采用显着的方式。另一方面,对不同类型的医学图像的访问已经激励了研究人员。结果,提出了一种庞大的技术数量。本文首先详细调了人工智能域中传统方法的背景知识。在此之后,我们会收集常用的数据集及其用例日期。此外,我们还显示了采用深入学习的机器学习的研究人员的百分比。因此,我们对这种情况进行了彻底的分析。最后,在研究挑战中,我们详细阐述了Covid-19研究中面临的问题,我们解决了我们的理解,以建立一个明亮健康的环境。
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深度学习属于人工智能领域,机器执行通常需要某种人类智能的任务。类似于大脑的基本结构,深度学习算法包括一种人工神经网络,其类似于生物脑结构。利用他们的感官模仿人类的学习过程,深入学习网络被送入(感官)数据,如文本,图像,视频或声音。这些网络在不同的任务中优于最先进的方法,因此,整个领域在过去几年中看到了指数增长。这种增长在过去几年中每年超过10,000多种出版物。例如,只有在医疗领域中的所有出版物中覆盖的搜索引擎只能在Q3 2020中覆盖所有出版物的子集,用于搜索术语“深度学习”,其中大约90%来自过去三年。因此,对深度学习领域的完全概述已经不可能在不久的将来获得,并且在不久的将来可能会难以获得难以获得子场的概要。但是,有几个关于深度学习的综述文章,这些文章专注于特定的科学领域或应用程序,例如计算机愿景的深度学习进步或在物体检测等特定任务中进行。随着这些调查作为基础,这一贡献的目的是提供对不同科学学科的深度学习的第一个高级,分类的元调查。根据底层数据来源(图像,语言,医疗,混合)选择了类别(计算机愿景,语言处理,医疗信息和其他工程)。此外,我们还审查了每个子类别的常见架构,方法,专业,利弊,评估,挑战和未来方向。
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Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.
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海洋生态系统及其鱼类栖息地越来越重要,因为它们在提供有价值的食物来源和保护效果方面的重要作用。由于它们的偏僻且难以接近自然,因此通常使用水下摄像头对海洋环境和鱼类栖息地进行监测。这些相机产生了大量数字数据,这些数据无法通过当前的手动处理方法有效地分析,这些方法涉及人类观察者。 DL是一种尖端的AI技术,在分析视觉数据时表现出了前所未有的性能。尽管它应用于无数领域,但仍在探索其在水下鱼类栖息地监测中的使用。在本文中,我们提供了一个涵盖DL的关键概念的教程,该教程可帮助读者了解对DL的工作原理的高级理解。该教程还解释了一个逐步的程序,讲述了如何为诸如水下鱼类监测等挑战性应用开发DL算法。此外,我们还提供了针对鱼类栖息地监测的关键深度学习技术的全面调查,包括分类,计数,定位和细分。此外,我们对水下鱼类数据集进行了公开调查,并比较水下鱼类监测域中的各种DL技术。我们还讨论了鱼类栖息地加工深度学习的新兴领域的一些挑战和机遇。本文是为了作为希望掌握对DL的高级了解,通过遵循我们的分步教程而为其应用开发的海洋科学家的教程,并了解如何发展其研究,以促进他们的研究。努力。同时,它适用于希望调查基于DL的最先进方法的计算机科学家,以进行鱼类栖息地监测。
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机器学习和非接触传感器的进步使您能够在医疗保健环境中理解复杂的人类行为。特别是,已经引入了几种深度学习系统,以实现对自闭症谱系障碍(ASD)等神经发展状况的全面分析。这种情况会影响儿童的早期发育阶段,并且诊断完全依赖于观察孩子的行为和检测行为提示。但是,诊断过程是耗时的,因为它需要长期的行为观察以及专家的稀缺性。我们展示了基于区域的计算机视觉系统的效果,以帮助临床医生和父母分析孩子的行为。为此,我们采用并增强了一个数据集,用于使用在不受控制的环境中捕获的儿童的视频来分析自闭症相关的动作(例如,在各种环境中使用消费级摄像机收集的视频)。通过检测视频中的目标儿童以减少背景噪声的影响,可以预处理数据。在时间卷积模型的有效性的推动下,我们提出了能够从视频帧中提取动作功能并通过分析视频中的框架之间的关系来从视频帧中提取动作功能并分类与自闭症相关的行为。通过对功能提取和学习策略的广泛评估,我们证明了通过膨胀的3D Convnet和多阶段的时间卷积网络实现最佳性能,达到了0.83加权的F1得分,以分类三种自闭症相关的动作,超越表现优于表现现有方法。我们还通过在同一系统中采用ESNET主链来提出一个轻重量解决方案,实现0.71加权F1得分的竞争结果,并在嵌入式系统上实现潜在的部署。
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人类行动识别是计算机视觉中的重要应用领域。它的主要目的是准确地描述人类的行为及其相互作用,从传感器获得的先前看不见的数据序列中。识别,理解和预测复杂人类行动的能力能够构建许多重要的应用,例如智能监视系统,人力计算机界面,医疗保健,安全和军事应用。近年来,计算机视觉社区特别关注深度学习。本文使用深度学习技术的视频分析概述了当前的动作识别最新识别。我们提出了识别人类行为的最重要的深度学习模型,并分析它们,以提供用于解决人类行动识别问题的深度学习算法的当前进展,以突出其优势和缺点。基于文献中报道的识别精度的定量分析,我们的研究确定了动作识别中最新的深层体系结构,然后为该领域的未来工作提供当前的趋势和开放问题。
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Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. This paper conducted an extensive literature review on the applications of computer vision in ITS and AD, and discusses challenges related to data, models, and complex urban environments. The data challenges are associated with the collection and labeling of training data and its relevance to real world conditions, bias inherent in datasets, the high volume of data needed to be processed, and privacy concerns. Deep learning (DL) models are commonly too complex for real-time processing on embedded hardware, lack explainability and generalizability, and are hard to test in real-world settings. Complex urban traffic environments have irregular lighting and occlusions, and surveillance cameras can be mounted at a variety of angles, gather dirt, shake in the wind, while the traffic conditions are highly heterogeneous, with violation of rules and complex interactions in crowded scenarios. Some representative applications that suffer from these problems are traffic flow estimation, congestion detection, autonomous driving perception, vehicle interaction, and edge computing for practical deployment. The possible ways of dealing with the challenges are also explored while prioritizing practical deployment.
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面部检测是为了在图像中搜索面部的所有可能区域,并且如果有任何情况,则定位面部。包括面部识别,面部表情识别,面部跟踪和头部姿势估计的许多应用假设面部的位置和尺寸在图像中是已知的。近几十年来,研究人员从Viola-Jones脸上检测器创造了许多典型和有效的面部探测器到当前的基于CNN的CNN。然而,随着图像和视频的巨大增加,具有面部刻度的变化,外观,表达,遮挡和姿势,传统的面部探测器被挑战来检测野外面孔的各种“脸部。深度学习技术的出现带来了非凡的检测突破,以及计算的价格相当大的价格。本文介绍了代表性的深度学习的方法,并在准确性和效率方面提出了深度和全面的分析。我们进一步比较并讨论了流行的并挑战数据集及其评估指标。进行了几种成功的基于深度学习的面部探测器的全面比较,以使用两个度量来揭示其效率:拖鞋和延迟。本文可以指导为不同应用选择合适的面部探测器,也可以开发更高效和准确的探测器。
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Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module.
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语义分割是图像的像素明智标记。由于在像素级别定义了问题,因此确定图像类标签是不可接受的,而是在原始图像像素分辨率下本地化它们是必要的。通过卷积神经网络(CNN)在创建语义,高级和分层图像特征方面的非凡能力推动;在过去十年中提出了几种基于深入的学习的2D语义分割方法。在本调查中,我们主要关注最近的语义细分科学发展,特别是在使用2D图像的基于深度学习的方法。我们开始分析了对2D语义分割的公共图像集和排行榜,概述了性能评估中使用的技术。在研究现场的演变时,我们按时间顺序分类为三个主要时期,即预先和早期的深度学习时代,完全卷积的时代和后FCN时代。我们在技术上分析了解决领域的基本问题的解决方案,例如细粒度的本地化和规模不变性。在借阅我们的结论之前,我们提出了一张来自所有提到的时代的方法表,每个方法都概述了他们对该领域的贡献。我们通过讨论现场当前的挑战以及他们已经解决的程度来结束调查。
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在视频中,人类的行为是三维(3D)信号。这些视频研究了人类行为的时空知识。使用3D卷积神经网络(CNN)研究了有希望的能力。 3D CNN尚未在静止照片中为其建立良好的二维(2D)等效物获得高输出。董事会3D卷积记忆和时空融合面部训练难以防止3D CNN完成非凡的评估。在本文中,我们实施了混合深度学习体系结构,该体系结构结合了Stip和3D CNN功能,以有效地增强3D视频的性能。实施后,在每个时空融合圈中进行训练的较详细和更深的图表。训练模型在处理模型的复杂评估后进一步增强了结果。视频分类模型在此实现模型中使用。引入了使用深度学习的多媒体数据分类的智能3D网络协议,以进一步了解人类努力中的时空关联。在实施结果时,著名的数据集(即UCF101)评估了提出的混合技术的性能。结果击败了提出的混合技术,该混合动力技术基本上超过了最初的3D CNN。将结果与文献的最新框架进行比较,以识别UCF101的行动识别,准确度为95%。
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The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.
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人群计数是公共场所情境意识的有效工具。使用图像和视频进行自动人群计数是一个有趣但充满挑战的问题,在计算机视觉中引起了极大的关注。在过去的几年中,已经开发了各种深度学习方法来实现最先进的表现。随着时间的流逝,这些方法在许多方面发生了变化,例如模型架构,输入管道,学习范式,计算复杂性和准确性提高等。在本文中,我们对人群计数领域中最重要的贡献进行了系统和全面的评论。 。尽管对该主题的调查很少,但我们的调查是最新的,并且在几个方面都不同。首先,它通过模型体系结构,学习方法(即损失功能)和评估方法(即评估指标)对最重要的贡献进行了更有意义的分类。我们选择了杰出和独特的作品,并排除了类似的作品。我们还通过基准数据集对著名人群计数模型进行分类。我们认为,这项调查可能是新手研究人员了解随着时间的推移和当前最新技术的逐步发展和贡献的好资源。
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我们提出了一种新的四管齐下的方法,在文献中首次建立消防员的情境意识。我们构建了一系列深度学习框架,彼此之叠,以提高消防员在紧急首次响应设置中进行的救援任务的安全性,效率和成功完成。首先,我们使用深度卷积神经网络(CNN)系统,以实时地分类和识别来自热图像的感兴趣对象。接下来,我们将此CNN框架扩展了对象检测,跟踪,分割与掩码RCNN框架,以及具有多模级自然语言处理(NLP)框架的场景描述。第三,我们建立了一个深入的Q学习的代理,免受压力引起的迷失方向和焦虑,能够根据现场消防环境中观察和存储的事实来制定明确的导航决策。最后,我们使用了一种低计算无监督的学习技术,称为张量分解,在实时对异常检测进行有意义的特征提取。通过这些临时深度学习结构,我们建立了人工智能系统的骨干,用于消防员的情境意识。要将设计的系统带入消防员的使用,我们设计了一种物理结构,其中处理后的结果被用作创建增强现实的投入,这是一个能够建议他们所在地的消防员和周围的关键特征,这对救援操作至关重要在手头,以及路径规划功能,充当虚拟指南,以帮助迷彩的第一个响应者恢复安全。当组合时,这四种方法呈现了一种新颖的信息理解,转移和综合方法,这可能会大大提高消防员响应和功效,并降低寿命损失。
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深度学习已被广​​泛用于医学图像分割,并且录制了录制了该领域深度学习的成功的大量论文。在本文中,我们使用深层学习技术对医学图像分割的全面主题调查。本文进行了两个原创贡献。首先,与传统调查相比,直接将深度学习的文献分成医学图像分割的文学,并为每组详细介绍了文献,我们根据从粗略到精细的多级结构分类目前流行的文献。其次,本文侧重于监督和弱监督的学习方法,而不包括无监督的方法,因为它们在许多旧调查中引入而且他们目前不受欢迎。对于监督学习方法,我们分析了三个方面的文献:骨干网络的选择,网络块的设计,以及损耗功能的改进。对于虚弱的学习方法,我们根据数据增强,转移学习和交互式分割进行调查文献。与现有调查相比,本调查将文献分类为比例不同,更方便读者了解相关理由,并将引导他们基于深度学习方法思考医学图像分割的适当改进。
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Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is comprised of two parts. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively.
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