空中无人机镜头的视觉检查是当今土地搜索和救援(SAR)运营的一个组成部分。由于此检查是对人类的缓慢而繁琐,令人疑惑的工作,我们提出了一种新颖的深入学习算法来自动化该航空人员检测(APD)任务。我们试验模型架构选择,在线数据增强,转移学习,图像平铺和其他几种技术,以提高我们方法的测试性能。我们将新型航空检验视网膜(空气)算法呈现为这些贡献的结合。空中探测器在精度(〜21个百分点增加)和速度方面,在常用的SAR测试数据上表现出最先进的性能。此外,我们为SAR任务中的APD问题提供了新的正式定义。也就是说,我们提出了一种新的评估方案,在现实世界SAR本地化要求方面排名探测器。最后,我们提出了一种用于稳健的新型后处理方法,近似对象定位:重叠边界框(MOB)算法的合并。在空中检测器中使用的最终处理阶段在真实的空中SAR任务面前显着提高了其性能和可用性。
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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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航空图像中的微小对象检测(TOD)是具有挑战性的,因为一个小物体只包含几个像素。最先进的对象探测器由于缺乏判别特征的监督而无法为微小对象提供令人满意的结果。我们的主要观察结果是,联合度量(IOU)及其扩展的相交对微小物体的位置偏差非常敏感,这在基于锚固的探测器中使用时会大大恶化标签分配的质量。为了解决这个问题,我们提出了一种新的评估度量标准,称为标准化的Wasserstein距离(NWD)和一个新的基于排名的分配(RKA)策略,以进行微小对象检测。提出的NWD-RKA策略可以轻松地嵌入到各种基于锚的探测器中,以取代标准的基于阈值的检测器,从而大大改善了标签分配并为网络培训提供了足够的监督信息。在四个数据集中测试,NWD-RKA可以始终如一地提高微小的对象检测性能。此外,在空中图像(AI-TOD)数据集中观察到显着的嘈杂标签,我们有动力将其重新标记并释放AI-TOD-V2及其相应的基准。在AI-TOD-V2中,丢失的注释和位置错误问题得到了大大减轻,从而促进了更可靠的培训和验证过程。将NWD-RKA嵌入探测器中,检测性能比AI-TOD-V2上的最先进竞争对手提高了4.3个AP点。数据集,代码和更多可视化可在以下网址提供:https://chasel-tsui.g​​ithub.io/ai/ai-tod-v2/
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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。
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随着全球的太阳能能力继续增长,越来越意识到先进的检验系统正度重视安排智能干预措施并最大限度地减少停机时间。在这项工作中,我们提出了一种新的自动多级模型,以通过使用YOLOV3网络和计算机视觉技术来检测由无人机捕获的空中图像上的面板缺陷。该模型结合了面板和缺陷的检测来改进其精度。主要的Noveltize由其多功能性来处理热量或可见图像,并检测各种缺陷及其对屋顶和地面安装的光伏系统和不同面板类型的缺陷。拟议的模型已在意大利南部的两个大型光伏工厂验证,优秀的AP至0.5超过98%,对于面板检测,卓越的AP@0.4(AP@0.5)大约为88.3%(66.95%)的热点红外热成像和MAP@0.5在可见光谱中近70%,用于检测通过污染和鸟粪诱导,分层,水坑的存在和覆盖屋顶板诱导的面板遮蔽的异常谱。还预测了对污染覆盖的估计。最后讨论了对不同yolov3的输出尺度对检测的影响的分析。
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在过去的十年中,由于航空图像引起的物体的规模和取向的巨大变化,对象检测已经实现了自然图像中的显着进展,而不是在空中图像中。更重要的是,缺乏大规模基准已成为在航拍图像(ODAI)中对物体检测发展的主要障碍。在本文中,我们在航空图像(DotA)中的物体检测和用于ODAI的综合基线的大规模数据集。所提出的DOTA数据集包含1,793,658个对象实例,18个类别的面向边界盒注释从11,268个航拍图像中收集。基于该大规模和注释的数据集,我们构建了具有超过70个配置的10个最先进算法的基线,其中已经评估了每个模型的速度和精度性能。此外,我们为ODAI提供了一个代码库,并建立一个评估不同算法的网站。以前在Dota上运行的挑战吸引了全球1300多队。我们认为,扩大的大型DOTA数据集,广泛的基线,代码库和挑战可以促进鲁棒算法的设计和对空中图像对象检测问题的可再现研究。
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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.
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尽管广泛用作可视检测任务的性能措施,但平均精度(AP)In(i)的限制在反映了本地化质量,(ii)对其计算的设计选择的鲁棒性以及其对输出的适用性没有信心分数。 Panoptic质量(PQ),提出评估Panoptic Seationation(Kirillov等,2019)的措施,不会遭受这些限制,而是限于Panoptic Seationation。在本文中,我们提出了基于其本地化和分类质量的视觉检测器的平均匹配误差,提出了定位召回精度(LRP)误差。 LRP错误,最初仅为Oksuz等人进行对象检测。 (2018),不遭受上述限制,适用于所有视觉检测任务。我们还介绍了最佳LRP(OLRP)错误,因为通过置信区获得的最小LRP错误以评估视觉检测器并获得部署的最佳阈值。我们提供对AP和PQ的LRP误差的详细比较分析,并使用七个可视检测任务(即对象检测,关键点检测,实例分割,Panoptic分段,视觉关系检测,使用近100个最先进的视觉检测器零拍摄检测和广义零拍摄检测)使用10个数据集来统一地显示LRP误差提供比其对应物更丰富和更辨别的信息。可用的代码:https://github.com/kemaloksuz/lrp-error
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Confluence是对对象检测的边界框后处理中的非墨西哥抑制(NMS)替代的新型非交流(IOU)替代方案。它克服了基于IOU的NMS变体的固有局限性,以通过使用归一化的曼哈顿距离启发的接近度度量来表示边界框聚类的更稳定,一致的预测指标来表示边界框群集。与贪婪和柔软的NMS不同,它不仅依赖分类置信度得分来选择最佳边界框,而是选择与给定群集中最接近其他盒子的框并删除高度汇合的相邻框。在MS Coco和CrowdHuman基准测试中,汇合的平均精度最高2.3-3.8%,而平均召回率则与DEACTO标准和ART NMS NMS变体相比,平均召回率最高为5.3-7.2%。广泛的定性分析和阈值灵敏度分析实验支持了定量结果,这支持了结论,即汇合比NMS变体更健壮。 Confluence代表边界框处理中的范式变化,有可能在边界框回归过程中替换IOU。
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每年,AEDESAEGYPTI蚊子都感染了数百万人,如登录,ZIKA,Chikungunya和城市黄热病等疾病。战斗这些疾病的主要形式是通过寻找和消除潜在的蚊虫养殖场来避免蚊子繁殖。在这项工作中,我们介绍了一个全面的空中视频数据集,获得了无人驾驶飞行器,含有可能的蚊帐。使用识别所有感兴趣对象的边界框手动注释视频数据集的所有帧。该数据集被用于开发基于深度卷积网络的这些对象的自动检测系统。我们提出了通过在可以注册检测到的对象的时空检测管道的对象检测流水线中的融合来利用视频中包含的时间信息,这些时间是可以注册检测到的对象的,最大限度地减少最伪正和假阴性的出现。此外,我们通过实验表明使用视频比仅使用框架对马赛克组成马赛克更有利。使用Reset-50-FPN作为骨干,我们可以分别实现0.65和0.77的F $ _1 $ -70分别对“轮胎”和“水箱”的对象级别检测,说明了正确定位潜在蚊子的系统能力育种对象。
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在现场遥远的小物体和物体的检测是监视应用中的一个重大挑战。此类对象由图像中的少量像素表示,并且缺乏足够的细节,因此很难使用常规检测器检测到它们。在这项工作中,提出了一个称为切片辅助超推理(SAHI)的开源框架,该框架提供了一种通用切片的辅助推理和用于小对象检测的微调管道。提出的技术是通用的,因为它可以在任何可用的对象检测器之上应用于而无需任何微调。实验评估,使用对象检测基线在Visdrone和Xview Aerial对象检测数据集上表明,FCO,VFNET和TOOD检测器分别将对象检测方法分别增加6.8%,5.1%和5.3%。此外,通过切片辅助微调可以进一步提高检测准确性,从而导致累计增加12.7%,13.4%和14.5%的AP按照相同的顺序。拟议的技术已与DestectRon2,MMDetection和Yolov5模型集成在一起,并在https://github.com/obss/sahi.git上公开获得。
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We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at:tinyurl.com/FCOSv1
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We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For 300 × 300 input, SSD achieves 74.3% mAP 1 on VOC2007 test at 59 FPS on a Nvidia Titan X and for 512 × 512 input, SSD achieves 76.9% mAP, outperforming a comparable state-of-the-art Faster R-CNN model. Compared to other single stage methods, SSD has much better accuracy even with a smaller input image size. Code is available at: https://github.com/weiliu89/caffe/tree/ssd .
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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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我们提出对象盒,这是一种新颖的单阶段锚定且高度可推广的对象检测方法。与现有的基于锚固的探测器和无锚的探测器相反,它们更偏向于其标签分配中的特定对象量表,我们仅将对象中心位置用作正样本,并在不同的特征级别中平均处理所有对象,而不论对象'尺寸或形状。具体而言,我们的标签分配策略将对象中心位置视为形状和尺寸不足的锚定,并以无锚固的方式锚定,并允许学习每个对象的所有尺度。为了支持这一点,我们将新的回归目标定义为从中心单元位置的两个角到边界框的四个侧面的距离。此外,为了处理比例变化的对象,我们提出了一个量身定制的损失来处理不同尺寸的盒子。结果,我们提出的对象检测器不需要在数据集中调整任何依赖数据集的超参数。我们在MS-Coco 2017和Pascal VOC 2012数据集上评估了我们的方法,并将我们的结果与最先进的方法进行比较。我们观察到,与先前的作品相比,对象盒的性能优惠。此外,我们执行严格的消融实验来评估我们方法的不同组成部分。我们的代码可在以下网址提供:https://github.com/mohsenzand/objectbox。
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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。
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最近的多目标跟踪(MOT)系统利用高精度的对象探测器;然而,培训这种探测器需要大量标记的数据。虽然这种数据广泛适用于人类和车辆,但其他动物物种显着稀缺。我们目前稳健的置信跟踪(RCT),一种算法,旨在保持鲁棒性能,即使检测质量差。与丢弃检测置信信息的先前方法相比,RCT采用基本上不同的方法,依赖于精确的检测置信度值来初始化曲目,扩展轨道和滤波器轨道。特别地,RCT能够通过有效地使用低置信度检测(以及单个物体跟踪器)来最小化身份切换,以保持对象的连续轨道。为了评估在存在不可靠的检测中的跟踪器,我们提出了一个挑战的现实世界水下鱼跟踪数据集,Fishtrac。在对FISHTRAC以及UA-DETRAC数据集的评估中,我们发现RCT在提供不完美的检测时优于其他算法,包括最先进的深单和多目标跟踪器以及更经典的方法。具体而言,RCT具有跨越方法的最佳平均热量,可以成功返回所有序列的结果,并且具有比其他方法更少的身份交换机。
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工业X射线分析在需要保证某些零件的结构完整性的航空航天,汽车或核行业中很常见。但是,射线照相图像的解释有时很困难,可能导致两名专家在缺陷分类上不同意。本文介绍的自动缺陷识别(ADR)系统将减少分析时间,还将有助于减少对缺陷的主观解释,同时提高人类检查员的可靠性。我们的卷积神经网络(CNN)模型达到94.2 \%准确性(MAP@iou = 50 \%),当应用于汽车铝铸件数据集(GDXRAR)时,它被认为与预期的人类性能相似,超过了当前状态该数据集的艺术。在工业环境上,其推理时间少于每个DICOM图像,因此可以安装在生产设施上,不会影响交付时间。此外,还进行了对主要高参数的消融研究,以优化从75 \%映射的初始基线结果最高94.2 \%map的模型准确性。
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In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its quality. While the commonly used threshold of 0.5 leads to noisy (low-quality) detections, detection performance frequently degrades for larger thresholds. This paradox of high-quality detection has two causes: 1) overfitting, due to vanishing positive samples for large thresholds, and 2) inference-time quality mismatch between detector and test hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds, is proposed to address these problems. The detectors are trained sequentially, using the output of a detector as training set for the next. This resampling progressively improves hypotheses quality, guaranteeing a positive training set of equivalent size for all detectors and minimizing overfitting. The same cascade is applied at inference, to eliminate quality mismatches between hypotheses and detectors. An implementation of the Cascade R-CNN without bells or whistles achieves state-of-the-art performance on the COCO dataset, and significantly improves high-quality detection on generic and specific object detection datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally, the Cascade R-CNN is generalized to instance segmentation, with nontrivial improvements over the Mask R-CNN. To facilitate future research, two implementations are made available at https://github.com/zhaoweicai/cascade-rcnn (Caffe) and https://github.com/zhaoweicai/Detectron-Cascade-RCNN (Detectron).
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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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