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.
translated by 谷歌翻译
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。
translated by 谷歌翻译
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。
translated by 谷歌翻译
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。
translated by 谷歌翻译
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。
translated by 谷歌翻译
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。
translated by 谷歌翻译
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.
translated by 谷歌翻译
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。
translated by 谷歌翻译
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。
translated by 谷歌翻译
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.
translated by 谷歌翻译
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.
translated by 谷歌翻译
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。
translated by 谷歌翻译
Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.
translated by 谷歌翻译
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.
translated by 谷歌翻译
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。
translated by 谷歌翻译
The task of locating and classifying different types of vehicles has become a vital element in numerous applications of automation and intelligent systems ranging from traffic surveillance to vehicle identification and many more. In recent times, Deep Learning models have been dominating the field of vehicle detection. Yet, Bangladeshi vehicle detection has remained a relatively unexplored area. One of the main goals of vehicle detection is its real-time application, where `You Only Look Once' (YOLO) models have proven to be the most effective architecture. In this work, intending to find the best-suited YOLO architecture for fast and accurate vehicle detection from traffic images in Bangladesh, we have conducted a performance analysis of different variants of the YOLO-based architectures such as YOLOV3, YOLOV5s, and YOLOV5x. The models were trained on a dataset containing 7390 images belonging to 21 types of vehicles comprising samples from the DhakaAI dataset, the Poribohon-BD dataset, and our self-collected images. After thorough quantitative and qualitative analysis, we found the YOLOV5x variant to be the best-suited model, performing better than YOLOv3 and YOLOv5s models respectively by 7 & 4 percent in mAP, and 12 & 8.5 percent in terms of Accuracy.
translated by 谷歌翻译
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。
translated by 谷歌翻译
在这项研究中,提出了一种集成检测模型,即Swin-Transformer-Yolov5或Swin-T-Yolov5,用于实时葡萄酒葡萄束检测,以继承Yolov5和Swin-Transformer的优势。该研究是针对2019年7月至9月的两种不同的霞多丽(始终白色或白色混合浆果皮肤)和梅洛(白色或白色混合浆果皮肤)的研究。从2019年7月至9月。 -yolov5,其性能与几个常用/竞争性对象探测器进行了比较,包括更快的R-CNN,Yolov3,Yolov4和Yolov5。在不同的测试条件下评估了所有模型,包括两个不同的天气条件(阳光和多云),两个不同的浆果成熟度(不成熟和成熟)以及三个不同的阳光方向/强度(早晨,中午和下午)进行全面比较。此外,Swin-t-Yolov5的预测葡萄束数量与地面真实值进行了比较,包括在注释过程中的现场手动计数和手动标记。结果表明,拟议的SWIN-T-YOLOV5的表现优于所有其他研究的葡萄束检测模型,当天气多云时,最高平均平均精度(MAP)和0.89的F1得分的97%。该地图分别比更快的R-CNN,Yolov3,Yolov4和Yolov5大约大约44%,18%,14%和4%。当检测到未成熟的浆果时,Swin-T-Yolov5获得了最低的地图(90%)和F1分数(0.82),其中该地图大约比相同的浆果大约40%,5%,3%和1%。此外,在将预测与地面真相进行比较时,Swin-T-Yolov5在Chardonnay品种上的表现更好,最多可达到R2的0.91和2.36根均方根误差(RMSE)。但是,它在Merlot品种上的表现不佳,仅达到R2和3.30的RMSE的0.70。
translated by 谷歌翻译
从汽车和交通检测到自动驾驶汽车系统,可以将街道对象的对象检测应用于各种用例。因此,找到最佳的对象检测算法对于有效应用它至关重要。已经发布了许多对象检测算法,许多对象检测算法比较了对象检测算法,但是很少有人比较了最新的算法,例如Yolov5,主要是侧重于街道级对象。本文比较了各种单阶段探测器算法; SSD MobilenetV2 FPN-Lite 320x320,Yolov3,Yolov4,Yolov5L和Yolov5S在实时图像中用于街道级对象检测。该实验利用了带有3,169张图像的修改后的自动驾驶汽车数据集。数据集分为火车,验证和测试;然后,使用重新处理,色相转移和噪音对其进行预处理和增强。然后对每种算法进行训练和评估。基于实验,算法根据推论时间及其精度,召回,F1得分和平均平均精度(MAP)产生了不错的结果。结果还表明,Yolov5L的映射@.5 of 0.593,MobileNetV2 FPN-Lite的推理时间最快,而其他推理时间仅为3.20ms。还发现Yolov5s是最有效的,其具有Yolov5L精度和速度几乎与MobilenetV2 FPN-Lite一样快。这表明各种算法适用于街道级对象检测,并且足够可行,可以用于自动驾驶汽车。
translated by 谷歌翻译
如今,使用微创手术(MIS)进行了更多的手术程序。这是由于其许多好处,例如最小的术后问题,较少的出血,较小的疤痕和快速的康复。但是,MIS的视野,小手术室和对操作场景的间接查看可能导致手术工具发生冲突并可能损害人体器官或组织。因此,通过使用内窥镜视频饲料实时检测和监视手术仪器,可以大大减少MIS问题,并且可以提高手术程序的准确性和成功率。在本文中,研究,分析和评估了对Yolov5对象检测器的一系列改进,以增强手术仪器的检测。在此过程中,我们进行了基于性能的消融研究,探索了改变Yolov5模型的骨干,颈部和锚固结构元素的影响,并注释了独特的内窥镜数据集。此外,我们将消融研究的有效性与其他四个SOTA对象探测器(Yolov7,Yolor,Scaled-Yolov4和Yolov3-SPP)进行了比较。除了Yolov3-SPP(在MAP中具有98.3%的模型性能和相似的推理速度)外,我们的所有基准模型(包括原始的Yolov5)在使用新的内窥镜数据集的实验中超过了我们的顶级精制模型。
translated by 谷歌翻译