尽管智能摄像头停车系统概念已经存在数十年，但一些方法已经完全解决了该系统的可扩展性和可靠性。由于智能停车系统的基石是检测占用率的能力，因此传统方法使用分类主链从手动标记的网格中预测斑点。这是耗时的，失去了系统的可扩展性。此外，大多数方法都使用深度学习模型，使其并非无错误，也不可靠。因此，我们提出了一个端到端的智能相机停车系统，在该系统中，我们提供了一个称为OCPDET的对象检测器的自主检测占用。我们的探测器还提供了对比模块的有意义的信息：训练和空间知识，这些知识避免了推理过程中的错误检测。我们在现有的PKLOT数据集上基于OCPDET进行基准OCPDET，并与传统的分类解决方案相比达到竞争成果。我们还引入了一个额外的SNU-SPS数据集，其中我们从各种视图中估算了系统性能，并在停车任务中进行系统评估。我们数据集的结果表明，我们的系统对现实世界应用有望。

技术的改进与时间和时间相关的问题线性相关。已经看到，随着时间的推移，人类面临的问题数量也会增加。然而，解决这些问题的技术也往往会改善。最早的现有问题之一开始于车辆的发明内容是停车位。多年来，使用技术的易于解决这个问题已经发展，但停车问题仍然仍未解决。这背后的主要原因是停车不仅涉及一个问题，而且它包括一系列问题。其中一个问题是分布式停车生态系统中停车槽的占用检测。在分布式系统中，用户将找到优选的停车位，而不是随机停车位。在本文中，我们将基于Web的应用提出了一种用于在不同停车位停车空间检测的解决方案。该解决方案基于计算机视觉（CV），并使用Python 3.0中编写的Django框架构建。解决方案用于解决占用检测问题以及提供用户基于可用性和偏好确定块的选项。我们提出的系统的评估结果是有前途和有效的。所提出的系统也可以与不同的系统集成，并用于解决其他相关停车问题。

Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this paper, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudo-box-based label assignment scheme that relaxes the constraints on scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposals for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model-driven and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared to advance further research in this field.

在世界各地的城市中，找到带有空置停车位的公共停车场是一个主要问题，使通勤时间耗费时间并增加交通拥堵。这项工作说明了如何使用手机摄像机的地理标签图像数据集，可用于导航到约翰内斯堡最方便的公共停车场，并带有可用的停车位，可由神经网络驱动的公共摄像头检测到。这些图像用于微调在Imagenet数据集上预先训练的检测模型，以证明对空置停车位的检测和分割，然后我们添加停车场的相应经度和纬度坐标，向基于驾驶员的最方便的停车场推荐停车位在距离距离和可用停车位数量上。使用VGG图像注释（VIA），我们使用来自扩展图像数据集的76张图像，并用四种不同类型的感兴趣对象的多边形大纲进行注释：汽车，开放式停车位，人员和汽车号码。我们使用细分模型来确保可以在生产中遮住数字板，以匿名使用汽车注册。我们在汽车和停车位上分别获得了89％和82％的交叉点。这项工作有可能帮助减少通勤者花费的时间来寻找免费的公共停车场，从而缓解购物综合大楼和其他公共场所的交通拥堵，并在公共道路上开车时最大程度地利用人们的实用性。

随着深度卷积神经网络的兴起，对象检测在过去几年中取得了突出的进步。但是，这种繁荣无法掩盖小物体检测（SOD）的不令人满意的情况，这是计算机视觉中臭名昭著的挑战性任务之一，这是由于视觉外观不佳和由小目标的内在结构引起的嘈杂表示。此外，用于基准小对象检测方法基准测试的大规模数据集仍然是瓶颈。在本文中，我们首先对小物体检测进行了详尽的审查。然后，为了催化SOD的发展，我们分别构建了两个大规模的小物体检测数据集（SODA），SODA-D和SODA-A，分别集中在驾驶和空中场景上。 SODA-D包括24704个高质量的交通图像和277596个9个类别的实例。对于苏打水，我们收集2510个高分辨率航空图像，并在9个类别上注释800203实例。众所周知，拟议的数据集是有史以来首次尝试使用针对多类SOD量身定制的大量注释实例进行大规模基准测试。最后，我们评估主流方法在苏打水上的性能。我们预计发布的基准可以促进SOD的发展，并产生该领域的更多突破。数据集和代码将很快在：\ url {https://shaunyuan22.github.io/soda}上。

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.

深神网络的对象探测器正在不断发展，并用于多种应用程序，每个应用程序都有自己的要求集。尽管关键安全应用需要高准确性和可靠性，但低延迟任务需要资源和节能网络。不断提出了实时探测器，在高影响现实世界中是必需的，但是它们过分强调了准确性和速度的提高，而其他功能（例如多功能性，鲁棒性，资源和能源效率）则被省略。现有网络的参考基准不存在，设计新网络的标准评估指南也不存在，从而导致比较模棱两可和不一致的比较。因此，我们对广泛的数据集进行了多个实时探测器（基于锚点，关键器和变压器）的全面研究，并报告了一系列广泛指标的结果。我们还研究了变量，例如图像大小，锚固尺寸，置信阈值和架构层对整体性能的影响。我们分析了检测网络的鲁棒性，以防止分配变化，自然腐败和对抗性攻击。此外，我们提供了校准分析来评估预测的可靠性。最后，为了强调现实世界的影响，我们对自动驾驶和医疗保健应用进行了两个独特的案例研究。为了进一步衡量关键实时应用程序中网络的能力，我们报告了在Edge设备上部署检测网络后的性能。我们广泛的实证研究可以作为工业界对现有网络做出明智选择的指南。我们还希望激发研究社区的设计和评估网络的新方向，该网络着重于更大而整体的概述，以实现深远的影响。

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.

车辆分类是一台热电电脑视觉主题，研究从地面查看到顶视图。在遥感中，顶视图的使用允许了解城市模式，车辆集中，交通管理等。但是，在瞄准像素方面的分类时存在一些困难：（a）大多数车辆分类研究使用对象检测方法，并且最公开的数据集设计用于此任务，（b）创建实例分段数据集是费力的，并且（C ）传统的实例分段方法由于对象很小，因此在此任务上执行此任务。因此，本研究目标是：（1）提出使用GIS软件的新型半监督迭代学习方法，（2）提出一种自由盒实例分割方法，（3）提供城市规模的车辆数据集。考虑的迭代学习程序：（1）标记少数车辆，（2）在这些样本上列车，（3）使用模型对整个图像进行分类，（4）将图像预测转换为多边形shapefile，（5 ）纠正有错误的一些区域，并将其包含在培训数据中，（6）重复，直到结果令人满意。为了单独的情况，我们考虑了车辆内部和车辆边界，DL模型是U-Net，具有高效网络B7骨架。当移除边框时，车辆内部变为隔离，允许唯一的对象识别。要恢复已删除的1像素边框，我们提出了一种扩展每个预测的简单方法。结果显示与掩模-RCNN（IOU中67％的82％）相比的更好的像素 - 明智的指标。关于每个对象分析，整体准确性，精度和召回大于90％。该管道适用于任何遥感目标，对分段和生成数据集非常有效。

由于其前所未有的优势，在规模，移动，部署和隐蔽观察能力方面，空中平台和成像传感器的快速出现是实现新的空中监测形式。本文从计算机视觉和模式识别的角度来看，全面概述了以人为本的空中监控任务。它旨在为读者提供使用无人机，无人机和其他空中平台的空中监测任务当前状态的深入系统审查和技术分析。感兴趣的主要对象是人类，其中要检测单个或多个受试者，识别，跟踪，重新识别并进行其行为。更具体地，对于这四项任务中的每一个，我们首先讨论与基于地面的设置相比在空中环境中执行这些任务的独特挑战。然后，我们审查和分析公共可用于每项任务的航空数据集，并深入了解航空文学中的方法，并调查他们目前如何应对鸟瞰挑战。我们在讨论缺失差距和开放研究问题的讨论中得出结论，告知未来的研究途径。

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. A dedicated venue for collecting and summarizing the latest advances of EVA is highly desired by the community. Besides, the basic concepts of EVA (e.g., definition, architectures, etc.) are ambiguous and neglected by these surveys due to the rapid development of this domain. A thorough clarification is needed to facilitate a consensus on these concepts. To fill in these gaps, we conduct a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.

计算机视觉在智能运输系统（ITS）和交通监视中发挥了重要作用。除了快速增长的自动化车辆和拥挤的城市外，通过实施深层神经网络的实施，可以使用视频监视基础架构进行自动和高级交通管理系统（ATM）。在这项研究中，我们为实时交通监控提供了一个实用的平台，包括3D车辆/行人检测，速度检测，轨迹估算，拥塞检测以及监视车辆和行人的相互作用，都使用单个CCTV交通摄像头。我们适应了定制的Yolov5深神经网络模型，用于车辆/行人检测和增强的排序跟踪算法。还开发了基于混合卫星的基于混合卫星的逆透视图（SG-IPM）方法，用于摄像机自动校准，从而导致准确的3D对象检测和可视化。我们还根据短期和长期的时间视频数据流开发了层次结构的交通建模解决方案，以了解脆弱道路使用者的交通流量，瓶颈和危险景点。关于现实世界情景和与最先进的比较的几项实验是使用各种交通监控数据集进行的，包括从高速公路，交叉路口和城市地区收集的MIO-TCD，UA-DETRAC和GRAM-RTM，在不同的照明和城市地区天气状况。

地理定位的概念是指确定地球上的某些“实体”的位置的过程，通常使用全球定位系统（GPS）坐标。感兴趣的实体可以是图像，图像序列，视频，卫星图像，甚至图像中可见的物体。由于GPS标记媒体的大规模数据集由于智能手机和互联网而迅速变得可用，而深入学习已经上升以提高机器学习模型的性能能力，因此由于其显着影响而出现了视觉和对象地理定位的领域广泛的应用，如增强现实，机器人，自驾驶车辆，道路维护和3D重建。本文提供了对涉及图像的地理定位的全面调查，其涉及从捕获图像（图像地理定位）或图像内的地理定位对象（对象地理定位）的地理定位的综合调查。我们将提供深入的研究，包括流行算法的摘要，对所提出的数据集的描述以及性能结果的分析来说明每个字段的当前状态。

自动化驾驶系统（广告）开辟了汽车行业的新领域，为未来的运输提供了更高的效率和舒适体验的新可能性。然而，在恶劣天气条件下的自主驾驶已经存在，使自动车辆（AVS）长时间保持自主车辆（AVS）或更高的自主权。本文评估了天气在分析和统计方式中为广告传感器带来的影响和挑战，并对恶劣天气条件进行了解决方案。彻底报道了关于对每种天气的感知增强的最先进技术。外部辅助解决方案如V2X技术，当前可用的数据集，模拟器和天气腔室的实验设施中的天气条件覆盖范围明显。通过指出各种主要天气问题，自主驾驶场目前正在面临，近年来审查硬件和计算机科学解决方案，这项调查概述了在不利的天气驾驶条件方面的障碍和方向的障碍和方向。

The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorithms. We investigate both the modular perception-planning-action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources and computational hardware. The comparison presented in this survey helps to gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices. 1

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

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。

遵循机器视觉系统在线自动化质量控制和检查过程的成功之后，这项工作中为两个不同的特定应用提供了一种对象识别解决方案，即，在医院准备在医院进行消毒的手术工具箱中检测质量控制项目，以及检测血管船体中的缺陷，以防止潜在的结构故障。该解决方案有两个阶段。首先，基于单镜头多伯克斯检测器（SSD）的特征金字塔体系结构用于改善检测性能，并采用基于地面真实的统计分析来选择一系列默认框的参数。其次，利用轻量级神经网络使用回归方法来实现定向检测结果。该方法的第一阶段能够检测两种情况下考虑的小目标。在第二阶段，尽管很简单，但在保持较高的运行效率的同时，检测细长目标是有效的。

自主车辆的环境感知受其物理传感器范围和算法性能的限制，以及通过降低其对正在进行的交通状况的理解的闭塞。这不仅构成了对安全和限制驾驶速度的重大威胁，而且它也可能导致不方便的动作。智能基础设施系统可以帮助缓解这些问题。智能基础设施系统可以通过在当前交通情况的数字模型的形式提供关于其周围环境的额外详细信息，填补了车辆的感知中的差距并扩展了其视野。数字双胞胎。然而，这种系统的详细描述和工作原型表明其可行性稀缺。在本文中，我们提出了一种硬件和软件架构，可实现这样一个可靠的智能基础架构系统。我们在现实世界中实施了该系统，并展示了它能够创建一个准确的延伸高速公路延伸的数字双胞胎，从而提高了自主车辆超越其车载传感器的极限的感知。此外，我们通过使用空中图像和地球观测方法来评估数字双胞胎的准确性和可靠性，用于产生地面真理数据。

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.