从现实世界中的图像(例如果园)中估算出准确可靠的水果和蔬菜计数,这是一个充满挑战的问题,最近引起了最近的关注。收获前估算水果计数为物流规划提供了有用的信息。尽管在水果检测方面已取得了很大进展,但估计实际计数仍然具有挑战性。实际上,水果通常聚集在一起。因此,仅检测水果的方法无法提供一般解决方案来估计准确的水果计数。此外,在园艺研究中,而不是单一的屈服估计中,更细致的信息,例如每个集群的苹果数量分布。在这项工作中,我们将图像从图像计算为多类分类问题,并通过训练卷积神经网络来解决它。我们首先评估方法的每图像精度,并将其与基于四个测试数据集的高斯混合模型的最先进方法进行比较。即使针对每个数据集专门调整了基于高斯混合模型的方法的参数,但我们的网络在四分之三数据集中的三个数据集中的表现最高为94 \%精度。接下来,我们使用该方法来估计两个数据集的产量,我们为此提供了真理。我们的方法达到了96-97 \%精度。有关更多详细信息,请在此处查看我们的视频:https://www.youtube.com/watch?v=le0mb5p-syc} {https://www.youtube.com/watch?v=le0mb5p-syc。
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产量估计是葡萄园管理中的强大工具,因为它允许种植者微调实践以优化产量和质量。但是,目前使用手动抽样进行估计,这是耗时和不精确的。这项研究表明,近端成像的应用与深度学习相结合,以进行葡萄园中的产量估计。使用车辆安装的传感套件进行连续数据收集,并使用商业收益率监控器在收获时结合了地面真实收益数据的收集,可以生成一个23,581个收益点和107,933张图像的大数据集。此外,这项研究是在机械管理的商业葡萄园中进行的,代表了一个充满挑战的图像分析环境,但在加利福尼亚中央山谷中的一组常见条件。测试了三个模型架构:对象检测,CNN回归和变压器模型。对象检测模型在手工标记的图像上进行了训练以定位葡萄束,并将束数量或像素区域求和以与葡萄产量相关。相反,回归模型端到端训练,以预测图像数据中的葡萄产量,而无需手动标记。结果表明,在代表性的保留数据集上,具有相当的绝对百分比误差为18%和18.5%的变压器和具有像素区域处理的对象检测模型。使用显着映射来证明CNN模型的注意力位于葡萄束的预测位置附近以及葡萄树冠的顶部。总体而言,该研究表明,近端成像和深度学习对于大规模预测葡萄群的适用性。此外,端到端建模方法能够与对象检测方法相当地执行,同时消除了手工标记的需求。
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水果和蔬菜的检测,分割和跟踪是精确农业的三个基本任务,实现了机器人的收获和产量估计。但是,现代算法是饥饿的数据,并非总是有可能收集足够的数据来运用最佳性能的监督方法。由于数据收集是一项昂贵且繁琐的任务,因此在农业中使用计算机视觉的能力通常是小企业无法实现的。在此背景下的先前工作之后,我们提出了一种初始弱监督的解决方案,以减少在精确农业应用程序中获得最新检测和细分所需的数据,在这里,我们在这里改进该系统并探索跟踪果实的问题果园。我们介绍了拉齐奥南部(意大利)葡萄的葡萄园案例,因为葡萄由于遮挡,颜色和一般照明条件而难以分割。当有一些可以用作源数据的初始标记数据(例如,葡萄酒葡萄数据)时,我们会考虑这种情况,但与目标数据有很大不同(例如表格葡萄数据)。为了改善目标数据的检测和分割,我们建议使用弱边界框标签训练分割算法,而对于跟踪,我们从运动算法中利用3D结构来生成来自已标记样品的新标签。最后,将两个系统组合成完整的半监督方法。与SOTA监督解决方案的比较表明,我们的方法如何能够训练以很少的标记图像和非常简单的标签来实现高性能的新型号。
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Accurate representation and localization of relevant objects is important for robots to perform tasks. Building a generic representation that can be used across different environments and tasks is not easy, as the relevant objects vary depending on the environment and the task. Furthermore, another challenge arises in agro-food environments due to their complexity, and high levels of clutter and occlusions. In this paper, we present a method to build generic representations in highly occluded agro-food environments using multi-view perception and 3D multi-object tracking. Our representation is built upon a detection algorithm that generates a partial point cloud for each detected object. The detected objects are then passed to a 3D multi-object tracking algorithm that creates and updates the representation over time. The whole process is performed at a rate of 10 Hz. We evaluated the accuracy of the representation on a real-world agro-food environment, where it was able to successfully represent and locate tomatoes in tomato plants despite a high level of occlusion. We were able to estimate the total count of tomatoes with a maximum error of 5.08% and to track tomatoes with a tracking accuracy up to 71.47%. Additionally, we showed that an evaluation using tracking metrics gives more insight in the errors in localizing and representing the fruits.
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通过丘陵形成的现场制备是一种常用的造林治疗,通过机械地创建称为丘的植物植物物质来改善树木生长条件。在现场准备之后,下一个关键步骤是计算土墩的数量,该堆积的数量为森林经理提供了对给定种植园块所需的幼苗数量的精确估计。计算土墩数量通常是通过林业工人的手动现场调查来进行的,林业工人昂贵且容易出错,尤其是在大面积地区。为了解决这个问题,我们提出了一个新颖的框架,利用无人机成像和计算机视觉的进步,以准确估计种植块上的土墩数量。提出的框架包括两个主要组件。首先,我们利用基于深度学习算法的视觉识别方法来通过基于像素的分割来进行多个对象检测。这使得可见的土墩以及其他经常看到的物体(例如树木,碎屑,水的积累)的初步计数可用于表征种植块。其次,由于视觉识别可能会受到几个扰动因子(例如丘陵侵蚀,遮挡)的限制,因此我们采用机器学习估计功能,该功能可预测基于第一阶段提取的局部块属性的最终数量。我们在新的无人机数据集上评估了所提出的框架,该数据集代表具有不同功能的众多种植块。所提出的方法在相对计数精度方面优于手动计数方法,表明它在困难情况下具有有利和有效的潜力。
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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.
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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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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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Furigana是日语写作中使用的发音笔记。能够检测到这些可以帮助提高光学特征识别(OCR)性能,或通过正确显示Furigana来制作日本书面媒体的更准确的数字副本。该项目的重点是在日本书籍和漫画中检测Furigana。尽管已经研究了日本文本的检测,但目前尚无提议检测Furigana的方法。我们构建了一个包含日本书面媒体和Furigana注释的新数据集。我们建议对此类数据的评估度量,该度量与对象检测中使用的评估协议类似,除非它允许对象组通过一个注释标记。我们提出了一种基于数学形态和连接组件分析的Furigana检测方法。我们评估数据集的检测,并比较文本提取的不同方法。我们还分别评估了不同类型的图像,例如书籍和漫画,并讨论每种图像的挑战。所提出的方法在数据集上达到76 \%的F1得分。该方法在常规书籍上表现良好,但在漫画和不规则格式的书籍上的表现较少。最后,我们证明所提出的方法可以在漫画109数据集上提高OCR的性能5 \%。源代码可通过\ texttt {\ url {https://github.com/nikolajkb/furiganadetection}}}
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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。
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我们介绍了Caltech Fish计数数据集(CFC),这是一个用于检测,跟踪和计数声纳视频中鱼类的大型数据集。我们将声纳视频识别为可以推进低信噪比计算机视觉应用程序并解决多对象跟踪(MOT)和计数中的域概括的丰富数据来源。与现有的MOT和计数数据集相比,这些数据集主要仅限于城市中的人和车辆的视频,CFC来自自然世界领域,在该域​​中,目标不容易解析,并且无法轻易利用外观功能来进行目标重新识别。 CFC允许​​研究人员训练MOT和计数算法并评估看不见的测试位置的概括性能。我们执行广泛的基线实验,并确定在MOT和计数中推进概括的最新技术的关键挑战和机会。
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Measuring growth rates of apple fruitlets is important because it allows apple growers to determine when to apply chemical thinners to their crops to optimize yield. The current practice of obtaining growth rates involves using calipers to record sizes of fruitlets across multiple days. Due to the number of fruitlets needed to be sized, this method is laborious, time-consuming, and prone to human error. In this paper, we present a computer vision approach to measure the sizes and growth rates of apple fruitlets. With images collected by a hand-held stereo camera, our system detects, segments, and fits ellipses to fruitlets to measure their diameters. To measure growth rates, we utilize an Attentional Graph Neural Network to associate fruitlets across different days. We provide quantitative results on data collected in an apple orchard, and demonstrate that our system is able to predict abscise rates within 3% of the current method with a 7 times improvement in speed, while requiring significantly less manual effort. Moreover, we provide results on images captured by a robotic system in the field, and discuss the next steps to make the process fully autonomous.
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The ability to capture detailed interactions among individuals in a social group is foundational to our study of animal behavior and neuroscience. Recent advances in deep learning and computer vision are driving rapid progress in methods that can record the actions and interactions of multiple individuals simultaneously. Many social species, such as birds, however, live deeply embedded in a three-dimensional world. This world introduces additional perceptual challenges such as occlusions, orientation-dependent appearance, large variation in apparent size, and poor sensor coverage for 3D reconstruction, that are not encountered by applications studying animals that move and interact only on 2D planes. Here we introduce a system for studying the behavioral dynamics of a group of songbirds as they move throughout a 3D aviary. We study the complexities that arise when tracking a group of closely interacting animals in three dimensions and introduce a novel dataset for evaluating multi-view trackers. Finally, we analyze captured ethogram data and demonstrate that social context affects the distribution of sequential interactions between birds in the aviary.
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We introduce an approach for recovering the 6D pose of multiple known objects in a scene captured by a set of input images with unknown camera viewpoints. First, we present a single-view single-object 6D pose estimation method, which we use to generate 6D object pose hypotheses. Second, we develop a robust method for matching individual 6D object pose hypotheses across different input images in order to jointly estimate camera viewpoints and 6D poses of all objects in a single consistent scene. Our approach explicitly handles object symmetries, does not require depth measurements, is robust to missing or incorrect object hypotheses, and automatically recovers the number of objects in the scene. Third, we develop a method for global scene refinement given multiple object hypotheses and their correspondences across views. This is achieved by solving an object-level bundle adjustment problem that refines the poses of cameras and objects to minimize the reprojection error in all views. We demonstrate that the proposed method, dubbed Cosy-Pose, outperforms current state-of-the-art results for single-view and multi-view 6D object pose estimation by a large margin on two challenging benchmarks: the YCB-Video and T-LESS datasets. Code and pre-trained models are available on the project webpage. 5
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The management of cattle over a huge area is still a challenging problem in the farming sector. With evolution in technology, Unmanned aerial vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative to manual animal censuses for livestock estimation since they are less risky and expensive.This paper evaluated and compared the cutting-edge object detection algorithms, YOLOv7,RetinaNet with ResNet50 backbone, RetinaNet with EfficientNet and mask RCNN. It aims to improve the occlusion problem that is to detect hidden cattle from a huge dataset captured by drones using deep learning algorithms for accurate cattle detection. Experimental results showed YOLOv7 was superior with precision of 0.612 when compared to the other two algorithms. The proposed method proved superior to the usual competing algorithms for cow face detection, especially in very difficult cases.
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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。
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跟踪和识别玩家是基于计算机视觉冰球分析的基本步骤。跟踪生成的数据用于许多其他下游任务,例如游戏事件检测和游戏策略分析。播放器跟踪和识别是一个具有挑战性的问题,因为与行人相比,曲棍球运动员的运动是快节奏和非线性的。还有显着的摄像头淘气和放大曲棍球广播视频。识别冰球中的玩家是挑战,因为同一团队的球员几乎相同,泽西号码是玩家之间唯一的鉴别因素。本文介绍了一种用于跟踪和识别广播NHL曲棍球视频中的玩家的自动化系统。该系统由三个组件(1)播放器跟踪组成,(2)团队识别和(3)播放器识别。由于没有公开可用的数据集,用于培训三个组件的数据集手动注释。利用艺术跟踪算法的状态来执行播放器跟踪,从而获得多目标跟踪精度(MOTA)得分为94.5%。对于团队识别,Away-Team Jerseys被分组为单一课程,并根据他们的泽西颜色在课堂上分组。然后在团队识别数据集上培训卷积神经网络。团队识别网络在测试集中获得97%的准确性。引入了一种新颖的播放器识别模型,其利用时间一维卷积网络来识别来自玩家边界框序列的玩家。播放器识别模型进一步利用了可用的NHL游戏名册数据,以获得83%的玩家识别精度。
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截至2017年,鱼类产品约占全球人类饮食的16%。计数作用是生产和生产这些产品的重要组成部分。种植者必须准确计算鱼类,以便这样做技术解决方案。开发了两个计算机视觉系统,以自动计算在工业池塘中生长的甲壳类幼虫。第一个系统包括带有3024x4032分辨率的iPhone 11摄像头,该摄像头在室内条件下从工业池塘中获取图像。使用该系统进行了两次实验,第一部实验包括在一天的增长阶段,在9,10的一天中使用iPhone 11相机在特定照明条件下获得的200张图像。在第二个实验中,用两个设备iPhone 11和索尼DSCHX90V摄像机拍摄了一个幼虫工业池。使用第一个设备(iPhone 11)测试了两个照明条件。在每种情况下,都获得了110张图像。该系统的准确性为88.4%的图像检测。第二个系统包括DSLR Nikon D510相机,具有2000x2000分辨率,在工业池塘外进行了七次实验。在幼虫生长阶段的第1天获取图像,从而获得了总共700张图像。该系统的密度为50的精度为86%。一种基于Yolov5 CNN模型开发的算法,该算法自动计算两种情况的幼虫数量。此外,在这项研究中,开发了幼虫生长函数。每天,从工业池塘手动取几个幼虫,并在显微镜下进行分析。确定生长阶段后,就获得了幼虫的图像。每个幼虫的长度都是通过图像手动测量的。最合适的模型是Gompertz模型,其拟合指数的良好性r平方为0.983。
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The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 3rd International Workshop on Reading Music Systems, held in Alicante on the 23rd of July 2021.
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The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the chal-
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