基于单眼的道路检测方法主要基于机器学习方法，依靠分类和提取精度以及外观，照明和天气变化。传统方法将预测引入条件随机字段或马尔可夫随机场模型中，以改善基于结构的中间预测。这些方法是基于优化的，因此资源很重且缓慢，使其不适合实时应用。我们提出了一种方法，可以通过具有基于超级像素的机器学习功能的本地专家的随机森林分类器来检测和细分道路。随机森林从预先训练的卷积神经网络-VGG-16中吸入机器学习的描述符。这些功能还集中在各自的超级像素中，从而使本地结构保持连续。我们将算法与基于Nueral网络的方法和传统方法（基于手工制作的功能）进行了比较，在结构化的道路（Camvid和Kitti）和非结构化的道路数据集上进行了比较。最后，我们介绍了一个带有1000个带注释的图像的道路场景数据集，并验证我们的算法在非城市和农村道路方案中效果很好。

Visual perception plays an important role in autonomous driving. One of the primary tasks is object detection and identification. Since the vision sensor is rich in color and texture information, it can quickly and accurately identify various road information. The commonly used technique is based on extracting and calculating various features of the image. The recent development of deep learning-based method has better reliability and processing speed and has a greater advantage in recognizing complex elements. For depth estimation, vision sensor is also used for ranging due to their small size and low cost. Monocular camera uses image data from a single viewpoint as input to estimate object depth. In contrast, stereo vision is based on parallax and matching feature points of different views, and the application of deep learning also further improves the accuracy. In addition, Simultaneous Location and Mapping (SLAM) can establish a model of the road environment, thus helping the vehicle perceive the surrounding environment and complete the tasks. In this paper, we introduce and compare various methods of object detection and identification, then explain the development of depth estimation and compare various methods based on monocular, stereo, and RDBG sensors, next review and compare various methods of SLAM, and finally summarize the current problems and present the future development trends of vision technologies.

通过卫星摄像机获取关于地球表面的大面积的信息使我们能够看到远远超过我们在地面上看到的更多。这有助于我们在检测和监测土地使用模式，大气条件，森林覆盖和许多非上市方面的地区的物理特征。所获得的图像不仅跟踪连续的自然现象，而且对解决严重森林砍伐的全球挑战也至关重要。其中亚马逊盆地每年占最大份额。适当的数据分析将有助于利用可持续健康的氛围来限制对生态系统和生物多样性的不利影响。本报告旨在通过不同的机器学习和优越的深度学习模型用大气和各种陆地覆盖或土地使用亚马逊雨林的卫星图像芯片。评估是基于F2度量完成的，而用于损耗函数，我们都有S形跨熵以及Softmax交叉熵。在使用预先训练的ImageNet架构中仅提取功能之后，图像被间接馈送到机器学习分类器。鉴于深度学习模型，通过传输学习使用微调Imagenet预训练模型的集合。到目前为止，我们的最佳分数与F2度量为0.927。

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。

手写数字识别（HDR）是光学特征识别（OCR）领域中最具挑战性的任务之一。不管语言如何，HDR都存在一些固有的挑战，这主要是由于个人跨个人的写作风格的变化，编写媒介和环境的变化，无法在反复编写任何数字等时保持相同的笔触。除此之外，特定语言数字的结构复杂性可能会导致HDR的模棱两可。多年来，研究人员开发了许多离线和在线HDR管道，其中不同的图像处理技术与传统的机器学习（ML）基于基于的和/或基于深度学习（DL）的体系结构相结合。尽管文献中存在有关HDR的广泛审查研究的证据，例如：英语，阿拉伯语，印度，法尔西，中文等，但几乎没有对孟加拉人HDR（BHDR）的调查，这缺乏对孟加拉语HDR（BHDR）的研究，而这些调查缺乏对孟加拉语HDR（BHDR）的研究。挑战，基础识别过程以及可能的未来方向。在本文中，已经分析了孟加拉语手写数字的特征和固有的歧义，以及二十年来最先进的数据集的全面见解和离线BHDR的方法。此外，还详细讨论了一些涉及BHDR的现实应用特定研究。本文还将作为对离线BHDR背后科学感兴趣的研究人员的汇编，煽动了对相关研究的新途径的探索，这可能会进一步导致在不同应用领域对孟加拉语手写数字进行更好的离线认识。

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.

Traffic signs are characterized by a wide variability in their visual appearance in real-world environments. For example, changes of illumination, varying weather conditions and partial occlusions impact the perception of road signs. In practice, a large number of different sign classes needs to be recognized with very high accuracy. Traffic signs have been designed to be easily readable for humans, who perform very well at this task. For computer systems, however, classifying traffic signs still seems to pose a challenging pattern recognition problem. Both image processing and machine learning algorithms are continuously refined to improve on this task. But little systematic comparison of such systems exist. What is the status quo? Do today's algorithms reach human performance? For assessing the performance of state-of-the-art machine learning algorithms, we present a publicly available traffic sign dataset with more than 50,000 images of German road signs in 43 classes. The data was considered in the second stage of the German Traffic Sign Recognition Benchmark held at IJCNN 2011. The results of this competition are reported and the best-performing algorithms are briefly described. Convolutional neural networks (CNNs) showed particularly high classification accuracies in the competition. We measured the performance of human subjects on the same data -and the CNNs outperformed the human test persons.

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.

作为许多自主驾驶和机器人活动的基本组成部分，如自我运动估计，障碍避免和场景理解，单眼深度估计（MDE）引起了计算机视觉和机器人社区的极大关注。在过去的几十年中，已经开发了大量方法。然而，据我们所知，对MDE没有全面调查。本文旨在通过审查1970年至2021年之间发布的197个相关条款来弥补这一差距。特别是，我们为涵盖各种方法的MDE提供了全面的调查，介绍了流行的绩效评估指标并汇总公开的数据集。我们还总结了一些代表方法的可用开源实现，并比较了他们的表演。此外，我们在一些重要的机器人任务中审查了MDE的应用。最后，我们通过展示一些有希望的未来研究方向来结束本文。预计本调查有助于读者浏览该研究领域。

We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images. We leverage knowledge of the problem's geometry to form a cost volume using deep feature representations. We learn to incorporate contextual information using 3-D convolutions over this volume. Disparity values are regressed from the cost volume using a proposed differentiable soft argmin operation, which allows us to train our method end-to-end to sub-pixel accuracy without any additional post-processing or regularization. We evaluate our method on the Scene Flow and KITTI datasets and on KITTI we set a new stateof-the-art benchmark, while being significantly faster than competing approaches.

自治机器人目前是最受欢迎的人工智能问题之一，在过去十年中，从自动驾驶汽车和人形系统到交付机器人和无人机，这是一项最受欢迎的智能问题。部分问题是获得一个机器人，以模仿人类的感知，我们的视觉感，用诸如神经网络等数学模型用相机和大脑的眼睛替换眼睛。开发一个能够在没有人为干预的情况下驾驶汽车的AI和一个小型机器人在城市中递送包裹可能看起来像不同的问题，因此来自感知和视觉的观点来看，这两个问题都有几种相似之处。我们目前的主要解决方案通过使用计算机视觉技术，机器学习和各种算法来实现对环境感知的关注，使机器人理解环境或场景，移动，调整其轨迹并执行其任务（维护，探索，等。）无需人为干预。在这项工作中，我们从头开始开发一个小型自动车辆，能够仅使用视觉信息理解场景，通过工业环境导航，检测人员和障碍，或执行简单的维护任务。我们审查了基本问题的最先进问题，并证明了小规模采用的许多方法类似于来自特斯拉或Lyft等公司的真正自动驾驶汽车中使用的方法。最后，我们讨论了当前的机器人和自主驾驶状态以及我们在这一领域找到的技术和道德限制。

使用（半）自动显微镜生成的大规模电子显微镜（EM）数据集已成为EM中的标准。考虑到大量数据，对所有数据的手动分析都是不可行的，因此自动分析至关重要。自动分析的主要挑战包括分析和解释生物医学图像的注释，并与实现高通量相结合。在这里，我们回顾了自动计算机技术的最新最新技术以及分析细胞EM结构的主要挑战。关于EM数据的注释，分割和可扩展性，讨论了过去五年来开发的高级计算机视觉，深度学习和软件工具。自动图像采集和分析的集成将允许用纳米分辨率对毫米范围的数据集进行高通量分析。

We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1]. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3], DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/.

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

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。

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。

现代车辆配备各种驾驶员辅助系统，包括自动车道保持，这防止了无意的车道偏离。传统车道检测方法采用了手工制作或基于深度的学习功能，然后使用基于帧的RGB摄像机进行通道提取的后处理技术。用于车道检测任务的帧的RGB摄像机的利用易于照明变化，太阳眩光和运动模糊，这限制了车道检测方法的性能。在自主驾驶中的感知堆栈中结合了一个事件摄像机，用于自动驾驶的感知堆栈是用于减轻基于帧的RGB摄像机遇到的挑战的最有希望的解决方案之一。这项工作的主要贡献是设计车道标记检测模型，它采用动态视觉传感器。本文探讨了使用事件摄像机通过设计卷积编码器后跟注意引导的解码器的新颖性应用了车道标记检测。编码特征的空间分辨率由致密的区域空间金字塔池（ASPP）块保持。解码器中的添加剂注意机制可提高促进车道本地化的高维输入编码特征的性能，并缓解后处理计算。使用DVS数据集进行通道提取（DET）的DVS数据集进行评估所提出的工作的功效。实验结果表明，多人和二进制车道标记检测任务中的5.54 \％$ 5.54 \％$ 5.54 \％$ 5.03 \％$ 5.03 \％$ 5.03。此外，在建议方法的联盟（$ iou $）分数上的交叉点将超越最佳最先进的方法，分别以6.50 \％$ 6.50 \％$ 6.5.37 \％$ 9.37 \％$ 。

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.

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.

由于价格合理的可穿戴摄像头和大型注释数据集的可用性，在过去几年中，Egintric Vision（又名第一人称视觉-FPV）的应用程序在过去几年中蓬勃发展。可穿戴摄像机的位置（通常安装在头部上）允许准确记录摄像头佩戴者在其前面的摄像头，尤其是手和操纵物体。这种内在的优势可以从多个角度研究手：将手及其部分定位在图像中；了解双手涉及哪些行动和活动；并开发依靠手势的人类计算机界面。在这项调查中，我们回顾了使用以自我为中心的愿景专注于手的文献，将现有方法分类为：本地化（其中的手或部分在哪里？）；解释（手在做什么？）；和应用程序（例如，使用以上为中心的手提示解决特定问题的系统）。此外，还提供了带有手基注释的最突出的数据集的列表。