自1800年代后期从墨西哥进入美国以来,棉花象鼻虫是Anthonomus Grandis Boheman是美国棉花行业的严重害虫,其损失超过160亿美元。这种害虫几乎被根除了。但是,得克萨斯州南部仍然面临这个问题,由于其亚热带气候可以全年生长,因此每年始终容易恢复有害生物。一旦到达销售虫(玉米),一旦它们到达销售虫的植物,志愿棉花(VC)植物一旦到达销子,可以作为这些害虫的宿主,一旦它们到达销钉头阶段(5-6叶阶段),因此需要检测到,位于,位于,位置,并被摧毁或喷涂。在本文中,我们介绍了一项研究,用于使用Yolov3在无人飞机系统(UAS)收集的三个频段航空图像上检测玉米田中的VC植物。本文的两倍目标是:(i)确定Yolov3是否可以使用UAS和(II)收集的RGB(红色,绿色和蓝色)在玉米场中进行VC检测来研究行为基于平均精度(AP),平均平均精度(MAP)和95%的95%的图像(320 x 320,s1; 416 x 416,s2; 416 x 416,s2;和512 x 512,s3像素)的图像上的yolov3的图像。信心水平。在三个量表之间,MAP没有显着差异,而S1和S3之间的AP存在显着差异(P = 0.04),S2和S3(P = 0.02)。 S2和S3之间的F1分数也存在显着差异(P = 0.02)。在所有三个量表上,MAP缺乏显着差异表明,训练有素的Yolov3模型可用于基于计算机视觉的远程试验的航空应用系统(RPAA),以实时实时实时进行VC检测和喷雾应用。
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Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.
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Boll Weevil(Anthonomus Grandis L.)是一种严重的害虫,主要以棉花为食。由于亚热带气候条件,在德克萨斯州的下里奥格兰德山谷等地方,棉花植物可以全年生长,因此,收获期间上一个季节的剩下的种子可以在玉米中的旋转中继续生长(Zea Mays L.)和高粱(高粱双色L.)。这些野性或志愿棉花(VC)植物到达Pinhead平方阶段(5-6叶阶段)可以充当Boll Weevil Pest的宿主。得克萨斯州的鲍尔象鼻虫根除计划(TBWEP)雇用人们在道路或田野侧面生长的风险投资和消除旋转作物的田间生长,但在田野中生长的植物仍未被发现。在本文中,我们证明了基于您的计算机视觉(CV)算法的应用,仅在三个不同的生长阶段(V3,V6)(V3,V6)中检测出在玉米场中生长的VC植物,以检测在玉米场中生长的VC植物的应用。使用无人飞机系统(UAS)遥感图像。使用Yolov5(S,M,L和X)的所有四个变体,并根据分类精度,平均平均精度(MAP)和F1得分进行比较。发现Yolov5s可以在玉米的V6阶段检测到最大分类精度为98%,地图为96.3%,而Yolov5s和Yolov5m的地图为96.3%,而Yolov5m的分类精度为85%,Yolov5m和Yolov5m的分类准确性最小,而Yolov5L的分类精度最少。在VT阶段,在尺寸416 x 416像素的图像上为86.5%。开发的CV算法有可能有效地检测和定位在玉米场中间生长的VC植物,并加快TBWEP的管理方面。
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为了控制棉花场中的鲍尔象鼻虫(Anthonomus Grandis L.)害虫重新感染,目前的志愿棉花(VC)(VC)(gossypium hirsutum L.)植物检测玉米(Zea Mays L.)和Sorghum等旋转作物中的植物检测(高粱双色L.)涉及在田野边缘的手动田地侦察。这导致许多风险植物在田野中间生长仍未被发现,并继续与玉米和高粱并肩生长。当他们到达Pinhead平方阶段(5-6片叶子)时,它们可以充当鲍尔维尔虫害的宿主。因此,需要检测,定位,然后精确地用化学物质进行斑点。在本文中,我们介绍了Yolov5M在放射线和伽马校正的低分辨率(1.2兆像素)的多光谱图像中的应用,以检测和定位在康沃尔场的流苏中间(VT)生长阶段生长的VC植物。我们的结果表明,可以以平均平均精度(地图)为79%,分类精度为78%,大小为1207 x 923像素的分类精度为78%,平均推理速度在NVIDIA上的平均推理速度接近47帧(FPS) NVIDIA JETSON TX2 GPU上的Tesla P100 GPU-16GB和0.4 fps。我们还证明了基于开发的计算机视觉(CV)算法的定制无人飞机系统(UAS)的应用应用程序应用程序,以及如何将其用于近乎实时检测和缓解玉米领域中VC植物的近乎实时检测和缓解为了有效地管理鲍尔象鼻虫害虫。
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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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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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现在,诸如无人机之类的无人机,从捕获和目标检测的各种目的中,从Ariel Imagery等捕获和目标检测的各种目的很大使用。轻松进入这些小的Ariel车辆到公众可能导致严重的安全威胁。例如,可以通过使用无人机在公共公共场合中混合的间谍来监视关键位置。在手中研究提出了一种改进和高效的深度学习自治系统,可以以极大的精度检测和跟踪非常小的无人机。建议的系统由自定义深度学习模型Tiny Yolov3组成,其中一个非常快速的物体检测模型的口味之一,您只能构建并用于检测一次(YOLO)。物体检测算法将有效地检测无人机。与以前的Yolo版本相比,拟议的架构表现出显着更好的性能。在资源使用和时间复杂性方面观察到改进。使用召回和精度分别为93%和91%的测量来测量性能。
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水果苍蝇是果实产量最有害的昆虫物种之一。在AlertTrap中,使用不同的最先进的骨干功能提取器(如MobiLenetv1和MobileNetv2)的SSD架构的实现似乎是实时检测问题的潜在解决方案。SSD-MobileNetv1和SSD-MobileNetv2表现良好并导致AP至0.5分别为0.957和1.0。YOLOV4-TINY优于SSD家族,在AP@0.5中为1.0;但是,其吞吐量速度略微慢。
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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。
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Asteroids are an indelible part of most astronomical surveys though only a few surveys are dedicated to their detection. Over the years, high cadence microlensing surveys have amassed several terabytes of data while scanning primarily the Galactic Bulge and Magellanic Clouds for microlensing events and thus provide a treasure trove of opportunities for scientific data mining. In particular, numerous asteroids have been observed by visual inspection of selected images. This paper presents novel deep learning-based solutions for the recovery and discovery of asteroids in the microlensing data gathered by the MOA project. Asteroid tracklets can be clearly seen by combining all the observations on a given night and these tracklets inform the structure of the dataset. Known asteroids were identified within these composite images and used for creating the labelled datasets required for supervised learning. Several custom CNN models were developed to identify images with asteroid tracklets. Model ensembling was then employed to reduce the variance in the predictions as well as to improve the generalisation error, achieving a recall of 97.67%. Furthermore, the YOLOv4 object detector was trained to localize asteroid tracklets, achieving a mean Average Precision (mAP) of 90.97%. These trained networks will be applied to 16 years of MOA archival data to find both known and unknown asteroids that have been observed by the survey over the years. The methodologies developed can be adapted for use by other surveys for asteroid recovery and discovery.
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Unmanned air vehicles (UAVs) popularity is on the rise as it enables the services like traffic monitoring, emergency communications, deliveries, and surveillance. However, the unauthorized usage of UAVs (a.k.a drone) may violate security and privacy protocols for security-sensitive national and international institutions. The presented challenges require fast, efficient, and precise detection of UAVs irrespective of harsh weather conditions, the presence of different objects, and their size to enable SafeSpace. Recently, there has been significant progress in using the latest deep learning models, but those models have shortcomings in terms of computational complexity, precision, and non-scalability. To overcome these limitations, we propose a precise and efficient multiscale and multifeature UAV detection network for SafeSpace, i.e., \textit{MultiFeatureNet} (\textit{MFNet}), an improved version of the popular object detection algorithm YOLOv5s. In \textit{MFNet}, we perform multiple changes in the backbone and neck of the YOLOv5s network to focus on the various small and ignored features required for accurate and fast UAV detection. To further improve the accuracy and focus on the specific situation and multiscale UAVs, we classify the \textit{MFNet} into small (S), medium (M), and large (L): these are the combinations of various size filters in the convolution and the bottleneckCSP layers, reside in the backbone and neck of the architecture. This classification helps to overcome the computational cost by training the model on a specific feature map rather than all the features. The dataset and code are available as an open source: github.com/ZeeshanKaleem/MultiFeatureNet.
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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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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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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。
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我们可以看到这一切吗?我们知道这一切吗?这些是我们当代社会中人类提出的问题,以评估我们解决问题的趋势。最近的研究探索了对象检测中的几种模型。但是,大多数人未能满足对客观性和预测准确性的需求,尤其是在发展中和发达国家中。因此,几种全球安全威胁需要开发有效解决这些问题的方法。本文提出了一种被称为智能监视系统(3S)的网络物理系统的对象检测模型。这项研究提出了一种2阶段的方法,突出了Yolo V3深度学习体系结构在实时和视觉对象检测中的优势。该研究实施了一种转移学习方法,以减少培训时间和计算资源。用于培训模型的数据集是MS COCO数据集,其中包含328,000个注释的图像实例。实施了深度学习技术,例如预处理,数据管道调查和检测,以提高效率。与其他新型研究模型相比,该模型的结果在检测监视镜头中的野生物体方面表现出色。记录了99.71%的精度,改进的地图为61.5。
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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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以知情方式监测和管理地球林是解决生物多样性损失和气候变化等挑战的重要要求。虽然森林评估的传统或空中运动提供了在区域一级分析的准确数据,但将其扩展到整个国家,以外的高度分辨率几乎不可能。在这项工作中,我们提出了一种贝叶斯深度学习方法,以10米的分辨率为全国范围的森林结构变量,使用自由可用的卫星图像作为输入。我们的方法将Sentinel-2光学图像和Sentinel-1合成孔径雷达图像共同变换为五种不同的森林结构变量的地图:95th高度百分位,平均高度,密度,基尼系数和分数盖。我们从挪威的41个机载激光扫描任务中培训和测试我们的模型,并证明它能够概括取消测试区域,从而达到11%和15%之间的归一化平均值误差,具体取决于变量。我们的工作也是第一个提出贝叶斯深度学习方法的工作,以预测具有良好校准的不确定性估计的森林结构变量。这些提高了模型的可信度及其适用于需要可靠的信心估计的下游任务,例如知情决策。我们提出了一组广泛的实验,以验证预测地图的准确性以及预测的不确定性的质量。为了展示可扩展性,我们为五个森林结构变量提供挪威地图。
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Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.
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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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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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