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.

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。

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.

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。

由于缺乏自动注释系统，大多数发展城市的城市机构都是数字未标记的。因此，在此类城市中，位置和轨迹服务（例如Google Maps，Uber等）仍然不足。自然场景图像中的准确招牌检测是从此类城市街道检索无错误的信息的最重要任务。然而，开发准确的招牌本地化系统仍然是尚未解决的挑战，因为它的外观包括文本图像和令人困惑的背景。我们提出了一种新型的对象检测方法，该方法可以自动检测招牌，适合此类城市。我们通过合并两种专业预处理方法和一种运行时效高参数值选择算法来使用更快的基于R-CNN的定位。我们采用了一种增量方法，通过使用我们构造的SVSO（Street View Signboard对象）签名板数据集，通过详细评估和与基线进行比较，以达到最终提出的方法，这些方法包含六个发展中国家的自然场景图像。我们在SVSO数据集和Open Image数据集上展示了我们提出的方法的最新性能。我们提出的方法可以准确地检测招牌（即使图像包含多种形状和颜色的多种嘈杂背景的招牌）在SVSO独立测试集上达到0.90 MAP（平均平均精度）得分。我们的实施可在以下网址获得：https：//github.com/sadrultoaha/signboard-detection

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.

面部检测是为了在图像中搜索面部的所有可能区域，并且如果有任何情况，则定位面部。包括面部识别，面部表情识别，面部跟踪和头部姿势估计的许多应用假设面部的位置和尺寸在图像中是已知的。近几十年来，研究人员从Viola-Jones脸上检测器创造了许多典型和有效的面部探测器到当前的基于CNN的CNN。然而，随着图像和视频的巨大增加，具有面部刻度的变化，外观，表达，遮挡和姿势，传统的面部探测器被挑战来检测野外面孔的各种“脸部。深度学习技术的出现带来了非凡的检测突破，以及计算的价格相当大的价格。本文介绍了代表性的深度学习的方法，并在准确性和效率方面提出了深度和全面的分析。我们进一步比较并讨论了流行的并挑战数据集及其评估指标。进行了几种成功的基于深度学习的面部探测器的全面比较，以使用两个度量来揭示其效率：拖鞋和延迟。本文可以指导为不同应用选择合适的面部探测器，也可以开发更高效和准确的探测器。

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。

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

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.

弹药废料检查是回收弹药金属废料的过程中的重要步骤。大多数弹药由许多组件组成，包括盒子，底漆，粉末和弹丸。包含能量学的弹药废料被认为是潜在危险的，应在回收过程之前分离。手动检查每片废料都是乏味且耗时的。我们已经收集了一个弹药组件的数据集，目的是应用人工智能自动对安全和不安全的废料进行分类。首先，通过弹药的视觉和X射线图像手动创建两个培训数据集。其次，使用直方图均衡，平均，锐化，功率定律和高斯模糊的空间变换来增强X射线数据集，以补偿缺乏足够的训练数据。最后，应用代表性的Yolov4对象检测方法用于检测弹药组件并分别将废料片分别为安全和不安全的类。训练有素的模型针对看不见的数据进行了测试，以评估应用方法的性能。实验证明了使用深度学习的弹药组件检测和分类的可行性。数据集和预培训模型可在https://github.com/hadi-ghnd/scrap-classification上获得。

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。

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.

We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learned simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks. In post-competition work, we establish a new state of the art for the detection task. Finally, we release a feature extractor from our best model called OverFeat.

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。

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。

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。

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.

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 .

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。