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摘要: On grounds of the advent of real-time applications, like autonomous driving, visual surveillance, and sports analysis, there is an augmenting focus of attention towards Multiple-Object Tracking (MOT). The tracking-by-detection paradigm, a commonly utilized approach, connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the similarities of the appearance or the motion between them. For an efficient detection and tracking of the numerous objects in 关键词: Multi-object detection; object tracking; feature extraction; morlet wavelet mutation (MWM); ant lion optimization (ALO); background subtraction
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摘要: Object detection and classification are the trending research topics in the field of computer vision because of their applications like visual surveillance. However, the vision-based objects detection and classification methods still suffer from detecting smaller objects and dense objects in the complex dynamic environment with high accuracy and precision. The present paper proposes a novel enhanced method to detect and classify objects using Hyperbolic Tangent based You Only Look Once V4 with a关键词: Object detection; hyperbolic tangent YOLO; manta-ray foraging; object classification
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Mahmood Al-Bahri;Abdelhamied Ateya;23;Ammar Muthanna;Abeer D. Algarni;Naglaa F. Soliman;4;
Intelligent Automation & Soft ComputingVolume 35, Issue 1, 2023, PP 97-110
摘要: The Internet of Things (IoT) is a recent technology, which implies the union of objects, “things”, into a single worldwide network. This promising paradigm faces many design challenges associated with the dramatic increase in the number of end-devices. Device identification is one of these challenges that becomes complicated with the increase of network devices. Despite this, there is still no universally accepted method of identifying things that would satisfy all requirements of the existing I关键词: Internet of things; identification; digital object architecture; handle system; security
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摘要: This paper discusses about the new approach of multiple object tracking relative to background information. The concept of multiple object tracking through background learning is based upon the theory of relativity, that involves a frame of reference in spatial domain to localize and/or track any object. The field of multiple object tracking has seen a lot of research, but researchers have considered the background as redundant. However, in object tracking, the background plays a vital role and 关键词: Object tracking; image processing; background learning; graph embedding algorithm; computer vision
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摘要: Bob Dylan’s classic song, “The Times They are A-Changin,” is used to emphasize the importance for child analytic educators to address the urgency for the profession and those training future practitioners to accept the need for evolutionary change. Refusing to change our traditional ways of work and training will lead child analysis to go the way of the dinosaurs. There is already ample evidence that our field is dying as manifested in decreasing numbers of child analytic cases, child analytic c关键词: Psychoanalytic frequency;parent work;integrated curricula;developmental object;mutative action
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摘要: Despite Ralph Greenson not being a child analyst, his two papers on the analysis of a gender-disordered boy offer a heuristic opportunity to consider current controversies in child analysis. Thus, his material is examined in terms of current considerations about how to understand and work with such disturbances, the distinctions between being a developmental object and providing a corrective emotional experience, the role and place of play in clinical technique, the analyst’s role as a real obje关键词: Gender disorder;nonlinear dynamic systems theory;developmental object;corrective emotional experience;play
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摘要: Recently, there was a lot of researches on real-time detection and tracking algorithms, as the frequent use of surveillance cameras and the expansion of its applications, especially in security and surveillance. However, many challenges have emerged that hinder monitoring systems' work, whether in the detection or tracking stage. We propose a robust new algorithm to detect and track objects from natural scenes captured with real-time cameras to achieve this. This work aims to create a detection 关键词: Multiple object detection;Multiple object tracking;Classification;Deep-learning;PCP
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摘要: Macroalgae biomass production, understood as cultivation and harvesting, is a minor industry in Europe at present, but the sector is recognized as having substantial growth potential. Here, we framed the environmental license as a boundary object between business and authorities and investigated the details of macroalgal licensing procedures in seven Northern European countries (Finland, Estonia, Sweden, Germany, Norway, Iceland, and Scotland). We conducted surveys and interviews with macroalgae关键词: Macroalgae;Cultivation;Harvesting;Licensing;Regulation;Boundary object
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摘要: With the development of deep learning technology, the research on convolutional neural network-based object detection is becoming more and more mature. However, most methods are unsatisfactory in dealing with the issue of semantic and spatial information imbalance. In this article, we extend the single-shot multibox detector SSD and propose a self-learning multi-scale object detection network by balancing the semantic information and spatial information, named SLMS-SSD. We first construct a shal关键词: Object detection;Deep learning;Multi-scale feature selection;Self-learning feature fusion
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摘要: Approximately 20 million people die every year of road accidents, mainly caused due to ignorance of road safety norms and traffic rules. Drivers’ experience and prudence still have to be relied upon to prevent vehicle accidents. Here, an algorithm is developed to prevent T-bone accidents, rear-end and head-on collisions, pedestrians accidents. Using Raspberry Pi 4 Model B with 8 GB RAM as processing unit, importing OpenCV library into python 3; an intensive dynamic real-time algorithm for the ef关键词: Accident prevention;Deep learning architecture;Repeated measures ANOVA;Real-time object identification;Single Shot Multibox Detection;ANOVA;RTCA Algorithm;EBD
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Tsokas Arsenios;Rysz Maciej;Pardalos Panos M.;Dipple Kathleen;
Expert Systems With ApplicationsVolume 205, Issue , 2022, PP
摘要: In this review, we present the main approaches developed around satellite and airborne Synthetic Aperture Radar (SAR) imagery. The great range of SAR imagery applications is summarized in this paper. We organize the most popular methods and their applications in a cohesive manner. SAR data applications are classified into earth observation and object detection applications and the former are separated into land, sea, and ice applications. We present the basic methodologies and recent advances in关键词: Synthetic Aperture Radar;Land classification;Object detection
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摘要: We find new classes of exact solutions to the Einstein field equations where the matter distribution satisfies a generalized polytropic equation of state. The matter distribution is uncharged with anisotropic pressures. Equations of state for polytropes and quark matter are contained as special cases. The matter variables and metric potentials can be obtained explicitly. Known solutions, for the choice of the gravitational potential made in this analysis, arise as special cases for particular ch关键词: Generalized polytropes;Einstein equations;Compact objects
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摘要: Object detection is advancing rapidly with the development of deep learning solutions and big data dimensions. This paper takes the challenging recognition task as the core work and proposes a novel and efficient network framework dedicated to unseen congestion detection. To guarantee the accuracy as well as the speed of inference, the detector utilizes the advanced You Only Look Once v4 (YOLOv4) as the backbone and agglutinates the four proposed strategies, called YOLO-Anti. Our model mainly co关键词: Deep learning;Congested and occluded objects;Object detection
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摘要: Domain adaptation for object detection (DAOD) has recently drawn much attention owing to its capability of detecting target objects without any annotations. To tackle the problem, previous works focus on aligning features extracted from partial levels (e.g., image-level, instance-level, RPN-level) in a two-stage detector via adversarial training. However, individual levels in the object detection pipeline are closely related to each other and this inter-level relation is unconsidered yet. To thi关键词: Domain adaptation;Object detection;Domain transferability;Domain shift
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摘要: Discovering unknown objects from visual information as curiosity is highly demanded for autonomous exploration in underwater environment. In this research, we propose an end-to-end deep neural network for anomaly detection in the highly dynamic unstructured underwater background faced by a moving robot. A novel patch-level autoencoder combined with a context-enhanced autoregressive network is introduced to differentiate abnormal patterns (unknowns) from normal ones (knowns) in fine-scale regions关键词: Anomaly detection;Learning unknown objects;Deep learning autoencoder;Autonomous underwater robotics
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摘要: Cotton is one of the most important cash and fiber crops in India. Agricultural machine learning plays a very important role in this agricultural industry. In this paper, the use of an object detection algorithm namely Mask RCNN along with transfer learning is experimented to find out if it is a fit algorithm to detect cotton leaf diseases in practical situations. The model training accuracy is found as 94 % whereas total loss value is continuously decreasing as number of optimize iterations are关键词: Detectron 2;Instance Segmentation;Mask RCNN;Object Detection;Transfer Learning
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摘要: Since traditional CNNs do not have explicit rotation and scale invariance, it is difficult to adapt to the scale changes of objects in real scenes when dealing with object detection tasks. There have been many researches on rotating object detection, which have achieved outstanding performance. But most of these works focus on anchor design, such as different multi-scale anchors, rotating BBox and rotating ROI Pooling. Although these methods have solved some practical problems in reality, the me关键词: Rotate object detection;Lightweight convolutional neural network;Self-attention;Mechanism;Spatial transformation network;Linear transformation;One-stage detector
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摘要: Feature fusion has been widely used for improving the tracking performance. However, how to effectively analyze the characteristics of different visual features to realize dynamical feature fusion is still a challenging task. In this paper, we propose a spatial-temporal context-based dynamic feature fusion method (STCDFF) with the correlation filters framework for object tracking. The proposed STCDFF method exploits spatial-temporal context to deeply analyze the characteristics of multiple visua关键词: Object tracking;Dynamic feature fusion;Spatial-temporal context;Correlation filters framework
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Journal
Chen Joya;Liu Dong;Luo Bin;Peng Xuezheng;Xu Tong;Chen Enhong;
Pattern RecognitionVolume 130, Issue , 2022, PP
摘要: As most object detectors rely on dense candidate samples to cover objects, they have always suffered from the extreme imbalance between very few foreground samples and numerous background samples during training, i.e., the foreground-background imbalance. Although several resampling and reweighting schemes (e.g., OHEM, Focal Loss, GHM) have been proposed to alleviate the imbalance, they are usually heuristic with multiple hyper-parameters, which is difficult to generalize on different object det关键词: Object detection;Class imbalance;Residual objectness
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摘要: Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc.. Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth information from images, representation learning from partially occluded unstructured po关键词: 3D object detection;Autonomous driving;Point clouds;02–07
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摘要: Advancements in mechatronics, perception and computational methods allowed robotics to focus on non-rigid part manipulation, which is characterized by high complexity due to the unpredictable and compliant behavior of flexible materials. This paper presents the automation of a manufacturing process involving linear non-rigid components through the implementation of a robotic system that addresses challenges related to flexible material perception and handling. The corresponding technological app关键词: Non-rigid;Flexible object;Object detection;Robotic manipulation;Process automation;Human robot collaboration
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摘要: Cross-modality fusing complementary information of multispectral remote sensing image pairs can improve the perception ability of detection algorithms, making them more robust and reliable for a wider range of applications, such as nighttime detection. Compared with prior methods, we think different features should be processed specifically, the modality-specific features should be retained and enhanced, while the modality-shared features should be cherry-picked from the RGB and thermal IR modal关键词: Cross-modality;Attention;Feature fusion;Object detection;Multispectral remote sensing imagery
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摘要: A novel case of combining deep learning and chemometrics for spectral image processing is presented. The case involved the application of deep transfer learning for detecting and locating the fruit centroid to extract pixels for spectral model development and application. The selected fruit case involved a non-symmetrical fruit pear where the interesting area for spectral model application is not the centroid of the whole fruit unlike fruit such as apples but the centroid of the belly part of th关键词: Computer vision;Artificial intelligence;Spectroscopy;Object detection
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摘要: The inner bore damage affects the launch performance and service life of electromagnetic railgun launcher. Detection and observation of railgun inner bore damage contribute to the study on mechanism and development rules of railgun damage. This paper analyzes five types of typical railgun inner bore damage. Based on the detection requirement for the damages, this paper proposes an automated damage detection system for the inner bore of electromagnetic railgun launcher consisting of data acquisit关键词: Railgun;Damage detection;Artificial neural networks;Object detection;Instance segmentation;Data augmentation
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摘要: Top Shaped Asteroids (TSAs) have proven to be common amongst the near-Earth rubble pile population, with multiple examples confirmed via groundbased radar and spaceborne optical sensors through the past 20 years. A substantial body of literature has developed, exploring the formation of these unique shapes either through rotation-induced landslides and creep, or collisional reaccumulation. Models of such mass movements can provide good explanations for mid and low latitude material redistributio关键词: Top Shaped Asteroids;Asteroid surfaces;Near-Earth objects;Bennu
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摘要: Vehicle damages are increasingly becoming a liability for shared mobility services. The large number of handovers between drivers demands for an accurate and fast inspection system, which locates small damages and classifies these into the correct damage category. To address this, a damage detection model is developed to locate vehicle damages and classify these into twelve categories. Multiple deep learning algorithms are used, and the effect of different transfer learning and training strategi关键词: Computer vision;Image recognition;Object detection;Deep learning;Vehicle damage detection
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摘要: Object tracking and motion detection are the major challenges in the real-time image and video processing applications. There are several tracking and prediction algorithms available to estimate and predict the state of a system. Kalman filter is the most widely used prediction algorithm as it is very simple, efficient and easy to implement for linear measurements. However, these types of filter algorithms are customized on hardware platforms such as Field-Programmable Gate Arrays (FPGAs) and Gr关键词: Kalman filter;Object tracking;Motion detection;FPGAs;GPUs;SOC
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摘要: Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. We propose to learn these filters as combinations of preset spectral filters defined by the Discrete Cosine Transform (DCT). Our proposed DCT-based harmonic blocks replace conventional convolutional layers to produce partially or fully harmonic versions of new or existing CNN architectures. Using DCT energy compaction properties, we demonstrate how the harmonic networks can be effi关键词: Harmonic network;Convolutional neural network;Discrete cosine transform;Image classification;Object detection;Semantic segmentation
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摘要: Moving Objects Segmentation (MOS) is a fundamental task in many computer vision applications such as human activity analysis, visual object tracking, content based video search, traffic monitoring, surveillance, and security. MOS becomes challenging due to abrupt illumination variations, dynamic backgrounds, camouflage and scenes with bootstrapping. To address these challenges we propose a MOS algorithm exploiting multiple adversarial regularizations including conventional as well as least squar关键词: Moving objects segmentation;Generative adversarial network;Background subtraction
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摘要: The time overhead is huge and the clustering quality is unstable when running the K-means algorithm on massive raw data. To solve these problems, the concept of the influence space is introduced, and on this basis, a new clustering algorithm named ISBFK-means based on the influence space is proposed in this paper. First, the influence space divides the given data set into multiple small regions. Then, the representative data objects in each region are obtained to form a new data set, in which th关键词: Clustering;Influence space;Region partition;Representative data objects
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