Lobster eye telescopes are ideal monitors to detect X-ray transients, because they could observe celestial objects over a wide field of view in X-ray band. However, images obtained by lobster eye telescopes are modified by their unique point spread functions, making it hard to design a high efficiency target detection algorithm. In this paper, we integrate several machine learning algorithms to build a target detection framework for data obtained by lobster eye telescopes. Our framework would firstly generate two 2D images with different pixel scales according to positions of photons on the detector. Then an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images. At last, a random forest algorithm will be used to pick up final detection results from candidates obtained by previous steps. Tested with simulated data of the Wide-field X-ray Telescope onboard the Einstein Probe, our detection framework could achieve over 94% purity and over 90% completeness for targets with flux more than 3 mCrab (9.6 * 10-11 erg/cm2/s) and more than 94% purity and moderate completeness for targets with lower flux at acceptable time cost. The framework proposed in this paper could be used as references for data processing methods developed for other lobster eye X-ray telescopes.

Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, which would lead to unprecedented opportunities for applying cluster-scale strong lenses to solve astrophysical and cosmological problems. However, the large dataset challenges astronomers to identify and extract strong lensing signals, particularly strongly lensed arcs, because of their complexity and variety. Hence, we propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images. We use the trained transformer to detect strongly lensed arcs from simulated and real data. Results show that our approach could achieve 99.63 % accuracy rate, 90.32 % recall rate, 85.37 % precision rate and 0.23 % false positive rate in detection of strongly lensed arcs from simulated images and could detect almost all strongly lensed arcs in real observation images. Besides, with an interpretation method, we have shown that our method could identify important information embedded in simulated data. Next step, to test the reliability and usability of our approach, we will apply it to available observations (e.g., DESI Legacy Imaging Surveys) and simulated data of upcoming large-scale sky surveys, such as the Euclid and the CSST.

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.

我们提出了一种基于机器学习的新型方法，用于从干涉数据中检测出星系尺度的重力透镜，特别是使用国际Lofar望远镜（ILT）采用的方法，该镜头是在150 MHz的频率上观察到北部无线电天空，该频率是350的角度分辨率。 MAS和90 Ujy Beam-1（1 Sigma）的灵敏度。我们开发并测试了几个卷积神经网络，以确定给定样品被归类为镜头或非镜头事件的概率和不确定性。通过对包括逼真的镜头和非镜头无线电源的模拟干涉成像数据集进行训练和测试，我们发现可以恢复95.3％的镜头样品（真正的正速率），仅污染仅为0.008来自非静态样品（假阳性速率）的含量。考虑到预期的镜头概率，结果导致了92.2％的镜头事件的样品纯度。我们发现，当镜头图像之间的最大图像分离大于合成光束尺寸的3倍时，网络结构是最健壮的，并且镜头图像具有至少与20个Sigma（点源）的总磁通密度相等）检测。对于ILT，这对应于爱因斯坦半径大于0.5 ARCSEC和一个无线电源群体的镜头样品，其150 MHz通量密度超过2 MJY。通过应用这些标准和我们的镜头检测算法，我们希望发现Lofar两米天空调查中包含的绝大多数星系尺度重力透镜系统。

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。

通过图像差异发现新瞬态的能力而无需直接人类干预是观察天文学的重要任务。对于此类图像分类问题，机器学习技术（例如卷积神经网络（CNN））表现出了显着的成功。在这项工作中，我们介绍了来自Dark Energy Survey Supernova计划（DES-SN）的CNN上的图像上自动瞬态识别的结果，其主要重点是使用IA型超新星用于宇宙学。通过对CNN进行架构搜索，我们可以从工件（图像缺陷，错误分配等）中确定有效选择非艺术的网络（例如，超新星，可变星，AGN等），可实现先前工作的效率在随机的森林中，无需花费任何特征识别的努力。 CNN还可以帮助我们确定一个标记错误的图像的子集。在此子集中对图像进行重新标记，与CNN的结果分类明显优于以前的结果。

尽管地面望远镜已经发现了许多近地的物体，但观测值却错过了一些快速移动的物体，尤其是那些近地检测限制的物体。我们开发了一个卷积神经网络，用于检测微弱的快速移动近地物体。它是通过模拟产生的人造条纹训练的，并且能够在模拟数据上找到这些小行星条纹的精度为98.7％，虚假正率为0.02％。该程序用于在2019年的四个晚上搜索来自Zwicky瞬态设施（ZTF）的图像数据，并确定了六个先前未被发现的小行星。我们的检测的视觉幅度范围为〜19.0-20.3，运动速率范围为〜6.8-24 dEG/天，与其他ZTF检测相比，这非常微弱。我们的小行星的大小也〜1-51 m，在近距离接近时〜5-60个月距距离〜5-60个月距离距离，假设其反照率值遵循已知的小行星的反照率分布函数。使用纯模拟的数据集来训练我们的模型，使该程序能够在检测微弱和快速移动的对象方面获得灵敏度，同时仍然能够恢复几乎所有使用真实检测来训练神经网络的神经网络几乎所有发现。我们的方法可以被任何观测员用于检测快速移动的小行星条纹。

居住在美国的每个妇女在8次发育侵袭性乳腺癌的可能性下有大约1。有丝分裂细胞计数是评估乳腺癌侵袭性或等级最常见的测试之一。在该预后，必须通过病理学家使用高分辨率显微镜检查组织病理学图像以计算细胞。不幸的是，可以是一种完整的任务，可重复性差，特别是对于非专家来说。最近深入学习网络适用于能够自动定位这些感兴趣区域的医学应用。然而，这些基于区域的网络缺乏利用通常用作唯一检测方法的完整图像CNN产生的分割特征的能力。因此，所提出的方法利用更快的RCNN进行对象检测，同时使用RGB图像特征的UNET产生的分割特征，以实现在Mitos-Atypia 2014分数上的F分数为0.508，计数数据集，优于最先进的攻击方法。

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.

The upcoming large scale surveys like LSST are expected to find approximately $10^5$ strong gravitational lenses by analysing data of many orders of magnitude larger than those in contemporary astronomical surveys. In this case, non-automated techniques will be highly challenging and time-consuming, even if they are possible at all. We propose a new automated architecture based on the principle of self-attention to find strong gravitational lenses. The advantages of self-attention-based encoder models over convolution neural networks are investigated, and ways to optimise the outcome of encoder models are analysed. We constructed and trained 21 self-attention based encoder models and five convolution neural networks to identify gravitational lenses from the Bologna Lens Challenge. Each model was trained separately using 18,000 simulated images, cross-validated using 2,000 images, and then applied to a test set with 100,000 images. We used four different metrics for evaluation: classification accuracy, area under the receiver operating characteristic curve (AUROC), the TPR$_0$ score and the TPR$_{10}$ score. The performances of self-attention-based encoder models and CNNs participating in the challenge are compared. They were able to surpass the CNN models that participated in the Bologna Lens Challenge by a high margin for the TPR$_0$ and TPR_${10}$. Self-Attention based models have clear advantages compared to simpler CNNs. They have highly competing performance in comparison to the currently used residual neural networks. Compared to CNNs, self-attention based models can identify highly confident lensing candidates and will be able to filter out potential candidates from real data. Moreover, introducing the encoder layers can also tackle the over-fitting problem present in the CNNs by acting as effective filters.

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.

实例对象检测在智能监视，视觉导航，人机交互，智能服务和其他字段中扮演重要作用。灵感来自深度卷积神经网络（DCNN）的巨大成功，基于DCNN的实例对象检测已成为一个有前途的研究主题。为了解决DCNN始终需要大规模注释数据集来监督其培训的问题，而手动注释是耗尽和耗时的，我们提出了一种基于共同训练的新框架，称为克自我标记和检测（Gram-SLD） 。建议的克拉姆-SLD可以自动注释大量数据，具有非常有限的手动标记的关键数据并实现竞争性能。在我们的框架中，克朗损失被定义并用于构造两个完全冗余和独立的视图和一个关键的样本选择策略以及自动注释策略，可以全面考虑精度并回忆，以产生高质量的伪标签。 Public Gmu厨房数据集的实验，活动视觉数据集和自制的Bhid-Item DataSetDemonstrite，只有5％的标记训练数据，我们的克斯LLD比较了对象检测中的竞争性能（少于2％的地图丢失）通过完全监督的方法。在具有复杂和变化环境的实际应用中，所提出的方法可以满足实例对象检测的实时和准确性要求。

The costs and impacts of government corruption range from impairing a country's economic growth to affecting its citizens' well-being and safety. Public contracting between government dependencies and private sector instances, referred to as public procurement, is a fertile land of opportunity for corrupt practices, generating substantial monetary losses worldwide. Thus, identifying and deterring corrupt activities between the government and the private sector is paramount. However, due to several factors, corruption in public procurement is challenging to identify and track, leading to corrupt practices going unnoticed. This paper proposes a machine learning model based on an ensemble of random forest classifiers, which we call hyper-forest, to identify and predict corrupt contracts in M\'exico's public procurement data. This method's results correctly detect most of the corrupt and non-corrupt contracts evaluated in the dataset. Furthermore, we found that the most critical predictors considered in the model are those related to the relationship between buyers and suppliers rather than those related to features of individual contracts. Also, the method proposed here is general enough to be trained with data from other countries. Overall, our work presents a tool that can help in the decision-making process to identify, predict and analyze corruption in public procurement contracts.

With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in area of animal face recognition were carried on pigs, cattle, sheep and other livestock. Face recognition consists of three sub-task: face detection, face normalizing and face identification. Most of animal face recognition study focuses on face detection and face identification. Animals are often uncooperative when taking photos, so the collected animal face images are often in arbitrary directions. The use of non-standard images may significantly reduce the performance of face recognition system. However, there is no study on normalizing of the animal face image with arbitrary directions. In this study, we developed a light-weight angle detection and region-based convolutional network (LAD-RCNN) containing a new rotation angle coding method that can detect the rotation angle and the location of animal face in one-stage. LAD-RCNN has a frame rate of 72.74 FPS (including all steps) on a single GeForce RTX 2080 Ti GPU. LAD-RCNN has been evaluated on multiple dataset including goat dataset and gaot infrared image. Evaluation result show that the AP of face detection was more than 95% and the deviation between the detected rotation angle and the ground-truth rotation angle were less than 0.036 (i.e. 6.48{\deg}) on all the test dataset. This shows that LAD-RCNN has excellent performance on livestock face and its direction detection, and therefore it is very suitable for livestock face detection and Normalizing. Code is available at https://github.com/SheepBreedingLab-HZAU/LAD-RCNN/

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

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。

在现代纺织工业中，服装项目的质量控制是必须的，因为消费者对最高标准的意识和期望不断增加，以支持可持续和道德的纺织品。从原材料到盒装股票，可以通过检查产品在其整个生命周期中检查其质量水平。检查可能包括颜色阴影测试，紧固件疲劳测试，织物称重测试，污染测试等。这项工作专门针对成品中小零件给出的污染物的自动检测，例如原材料，例如小石头和塑料碎片或材料从施工过程中，例如整针或夹子。识别是通过对项目的X射线图像进行两级处理来执行的：在第一个中，多阈值分析识别灰度和形状属性的污染；第二层由一个深度学习分类器组成，该分类器经过训练，以区分真正的阳性和误报。由于结果满足了该过程的技术规范，即自动检测器成功地部署在实际生产工厂中，即小于3％的虚假负面因素和小于15％的虚假阳性。

近年来，卷积神经网络（CNN）在合成孔径雷达（SAR）目标识别方面表现出巨大的潜力。 SAR图像具有强烈的粒度感，并且具有不同的纹理特征，例如斑点噪声，目标优势散射器和目标轮廓，这些轮廓很少在传统的CNN模型中被考虑。本文提出了两个残留块，即具有多尺度接收场（RFS）的EMC2A块，基于多型结构，然后设计了有效的同位素体系结构深CNN（DCNN），EMC2A-net。 EMC2A阻止使用不同的扩张速率利用平行的扩张卷积，这可以有效地捕获多尺度上下文特征而不会显着增加计算负担。为了进一步提高多尺度功能融合的效率，本文提出了多尺度特征跨通道注意模块，即EMC2A模块，采用了局部的多尺度特征交互策略，而无需降低维度。该策略通过有效的一维（1D） - 圆形卷积和Sigmoid函数适应每个通道的权重，以指导全球通道明智的关注。 MSTAR数据集上的比较结果表明，EMC2A-NET优于相同类型的现有模型，并且具有相对轻巧的网络结构。消融实验结果表明，仅使用一些参数和适当的跨渠道相互作用，EMC2A模块可显着提高模型的性能。