Separate Objects Research Articles

Symmetric Separable Convex Resource Allocation Problems with Structured Disjoint Interval Bound Constraints

Motivated by the problem of scheduling electric vehicle (EV) charging with a minimum charging threshold in smart distribution grids, we introduce the resource allocation problem (RAP) with a symmetric separable convex objective function and disjoint interval bound constraints. In this RAP, the aim is to allocate an amount of resource over a set of n activities, in which each individual allocation is restricted to a disjoint collection of m intervals. This is a generalization of classic RAPs studied in the literature in which, in contrast, each allocation is only restricted by simple lower and upper bounds, that is, m = 1. We propose an exact algorithm that, for four special cases of the problem, returns an optimal solution in [Formula: see text] time, where the term nF represents the number of flops required for one evaluation of the separable objective function. In particular, the algorithm runs in polynomial time when the number of intervals m is fixed. Moreover, we show how this algorithm can be adapted also to output an optimal solution to the problem with integer variables without increasing its time complexity. Computational experiments demonstrate the practical efficiency of the algorithm for small values of m and, in particular, for solving EV charging problems. History: Accepted by Antonia Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0263 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0263 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

New watershed methods for isolating and characterizing discrete objects in 3D data sets

This paper introduces new algorithms for conducting and improving watershed analysis, implemented with the particular goal of improving the ability to measure the shapes of mineral grains to be subsequently be analyzed by mass spectrometry. This application requires a high degree of accuracy and fidelity in terms of both separating all touching grains and preserving their shapes. The algorithms are designed to take advantage of a vector-based programming environment. A new implementation of the Euclidean distance transform utilizes the fact that the distance from any adjacent pair of voxels to the nearest boundary must be within one voxel of each other. In practice, however, this algorithm is outperformed by a smoothed approximate distance transform that is faster to compute and results in less irregular watershed boundaries. A one-pass rainfall-based watershed algorithm is introduced that runs in linear time with the number of segmented voxels, and requires no priority queue. Unlike marker-based watershed algorithms based on the basin-filling approach, the rainfall approach finds watersheds associated with all local maxima in the distance map, even if a marking algorithm is used. A post-watershed smoothing algorithm improves watershed boundaries and eliminates small spurious watersheds. The one-pass watershed and post-watershed smoothing algorithms run in times superior or comparable to basin-fill watershed algorithms implemented in other environments, and offers excellent ability to separate touching objects efficiently while placing watershed boundaries that maximize the preservation of details of particle shape. Further time improvement could come from implementing them in a vector-based environment that allows explicit multi-threading.

Multiple-objective control of stormwater basins using deep reinforcement learning

ABSTRACT Stormwater basins are important stormwater control measures which can reduce peak flow rate, mitigate flooding volume, and improve water quality during heavy rainfall events. Previous control strategies for stormwater basins have typically treated water quality and quantity as separate objectives. With the increasing urban runoff caused by climate change and urbanization, current single-objective control strategies cannot fully harness the control potential of basins and therefore require improvement. However, designing multi-objective control strategies for basins is challenging because of the conflicting operation goals and the complexity of the dynamic environmental conditions. This research proposes a novel real-time control strategy based on deep reinforcement learning to address these challenges. It employs a deep Q-network to develop an agent capable of making control decisions. After being trained on three different rainfall events, the reinforcement learning agent can make appropriate decisions for previously unseen rainfall events. Compared to the other two rule-based control scenarios and a static state scenario, the deep reinforcement learning method is more effective in terms of reducing total suspended solids, reducing peak flow, minimizing outflow flashiness, and controlling effort, striking a good balance between conflicting control objectives.

Frequency dependence of reflections on radar landmarks

Purpose. Reducing the dispersion of radar reflections from local objects, with multi-frequency sensing, to solve the problem of orientation by radar reflections from objects. Methodology. Reflections from local objects in the entire frequency range of the radar station (RS) at the same radio engineering position were measured by three independent radars of the same type on different days and at different antenna elevations. The deviation of the radar stations in the position did not exceed 500 meters. Coordinates (azimuth, range) of reflections of several separate local objects were allocated for each radar. The average values of reflections from local objects and their dispersion in the frequency range were calculated. Using various algorithms, individual frequencies were sampled and the reflected signals were averaged at these frequencies. The decrease in the dispersion of the reflected signal from the number of frequencies at which reflections were measured and from the algorithm for selecting these frequencies was investigated. Findings. Averaging the values of reflections from local objects for several frequencies leads to a decrease in dispersion and, as a result, to a more accurate correspondence of the reflected signal level to the geometric size of the local object. The variance decreases most rapidly for a small number of frequencies selected for averaging when selecting frequencies located in an interval of at least 1% relative to each other. Originality. To solve the problem of orientation based on radar reflections from local objects, it is necessary to identify the landmarks selected on a digital terrain model. Due to the fact that local objects (hills) are a collection of many reflectors falling into the allowed volume of the radar, with different levels of reflections and random phases, there may not be radar reflection from a local object at a certain frequency, or it may be very small. In order to unambiguously identify all landmarks, measurements must be carried out at several frequencies. The work has established how many frequencies measurements should be performed at and on what principle these frequencies should be selected. Practical value. The advent of digital terrain models made it possible to solve the problem of terrain orientation by comparing radar reflections from local objects with reflection models based on digital terrain maps. Radar reflection models use mathematical expectations of reflection values, unlike real reflections, which have random deviations in signal levels depending on the operating frequency. Reducing the variance of these deviations increases the accuracy of identifying characteristic local objects (landmarks) used to orient the radar in the absence of data from satellite navigation systems.

End-to-end multiple object tracking in high-resolution optical sensors of drones with transformer models

Drone aerial imaging has become increasingly important across numerous fields as drone optical sensor technology continues to advance. One critical challenge in this domain is achieving both accurate and efficient multi-object tracking. Traditional deep learning methods often separate object identification from tracking, leading to increased complexity and potential performance degradation. Conventional approaches rely heavily on manual feature engineering and intricate algorithms, which can further limit efficiency. To overcome these limitations, we propose a novel Transformer-based end-to-end multi-object tracking framework. This innovative method leverages self-attention mechanisms to capture complex inter-object relationships, seamlessly integrating object detection and tracking into a unified process. By utilizing end-to-end training, our approach simplifies the tracking pipeline, leading to significant performance improvements. A key innovation in our system is the introduction of a trajectory detection label matching technique. This technique assigns labels based on a comprehensive assessment of object appearance, spatial characteristics, and Gaussian features, ensuring more precise and logical label assignments. Additionally, we incorporate cross-frame self-attention mechanisms to extract long-term object properties, providing robust information for stable and consistent tracking. We further enhance tracking performance through a newly developed self-characteristics module, which extracts semantic features from trajectory information across both current and previous frames. This module ensures that the long-term interaction modules maintain semantic consistency, allowing for more accurate and continuous tracking over time. The refined data and stored trajectories are then used as input for subsequent frame processing, creating a feedback loop that sustains tracking accuracy. Extensive experiments conducted on the VisDrone and UAVDT datasets demonstrate the superior performance of our approach in drone-based multi-object tracking.

Investigating Alternative Representations of Building Structures in Traffic Noise Modeling

This paper presents the results of an investigation of various traffic noise modeling techniques with the Traffic Noise Model (TNM) 2.5 software package, with specific focus on the representation of traffic noise shielding from residences parallel to the freeway. A comprehensive traffic noise field study was undertaken in a neighborhood adjacent to Interstate 270 in Columbus, Ohio. Data collection activities occurred in three “waves” accounting for the pre-clearing, no barrier, and with barrier conditions. Measured noise levels at the study site indicated that there was some noise shielding effect of the residential structures; this effect was most readily perceptible behind the first row of residences. Separate TNM layouts were developed to represent the neighborhood’s residential structures as either a TNM building row object or as separate TNM barrier objects for each structure. The analysis found that modeling each individual residential structure as a separate TNM noise barrier object (single building façade that is closest to the freeway) produces the most accurate modeled noise levels, as compared to the building row representation or simply omitting the shielding from structures altogether. Based on these findings, it is recommended that traffic noise practitioners consider modeling each residential structure in noise-sensitive areas as TNM noise barrier objects.

Enhancing resilience and reducing waste in food supply chains: a systematic review and future directions leveraging emerging technologies

ABSTRACT The sustainability of food supply chains is gaining increasing attention, particularly after the COVID-19 pandemic. A food supply system that simultaneously prioritises resilience and minimises wastage is crucial. It is found that many studies have explored reducing food waste and increasing supply chain resilience as separate objectives, but research is scarce investigating both objectives in conjunction. This paper presents a comprehensive systematic review focusing on existing solutions to reducing food waste and enhancing resilience. It discusses future directions, particularly leveraging emerging technologies such as the Internet of Things, blockchain, artificial intelligence, and machine learning. The studies are categorised into three main topics: crisis resilience solutions, food waste reduction solutions, and integrated solutions. The literature review reveals a lack of empirical and practical solutions for concurrently improving resilience and decreasing food waste. Further in-depth investigations are necessary to propose practical and integrated approaches that tackle both challenges jointly.

Oppositional arithmetic optimization algorithm for network reconfiguration and simultaneous placement of DG and capacitor in radial distribution networks

AbstractThe prime objective of this study is the simultaneous network reconfiguration with distributed generation (DG) and capacitor placement in radial distribution networks (RDN) to get the techno and economic benefits for two separate objectives, which are the minimization of actual power loss and annual economic loss as well as a multi objective combining these two single objectives using an oppositional arithmetic optimization algorithm (OAOA). It is an improved version of the currently suggested arithmetic optimization algorithm (AOA) used in the field of engineering for the optimization task. Though the recently developed AOA shows its efficacy in different optimization tasks but to improve the quality of solutions, convergence behavior, and to avoid the local optima, oppositional behavior is added to AOA. The efficacy and exactness of OAOA are tested on three test systems (33‐bus, 69‐bus, and 118‐bus). For the reduction of power loss and annual economic loss as well as the multi objective optimization, two scenarios with different cases are executed using OAOA in RDNs. In scenario 1, the installation of the capacitor (case 1), the installation of unity power factor (UPF) based DG (case 2), and the placement of optimal power factor (OPF) based DG (case 3) have been executed. In scenario 2, allocation of UPF based DG and capacitors simultaneously (case 1), placement of OPF based DG and capacitors simultaneously (case 2) and simultaneous reconfiguration with installation of OPF based DG and capacitor (case 3) has been executed. This recommended OAOA algorithm provides the percentage improvement in real power loss and yearly economic loss for all cases of 33‐bus, and 69‐bus systems (34.28%, 65.50%, 94.43%, 93.26%, 94.89%, and 95.11%), (28.54%, 56.69%, 83.42%, 79.62%, 83.65%, and 83.71%), and (35.51%, 69.16%, 98.10%, 97.52%, 98.22%, and 98.25%), (30.26%, 61.68%, 88.75%, 85.42%, 88.81%, and 88.98%), respectively. The results and comparative study reveal that the OAOA is better than several optimization algorithms in terms of solution quality and good results. This algorithm has a good speed of response and convergence behavior.

Sparse Integer Programming Is Fixed-Parameter Tractable

We study the general integer programming problem where the number of variables n is a variable part of the input. We consider two natural parameters of the constraint matrix A: its numeric measure a and its sparsity measure d. We present an algorithm for solving integer programming in time [Formula: see text], where g is some computable function of the parameters a and d, and L is the binary encoding length of the input. In particular, integer programming is fixed-parameter tractable parameterized by a and d, and is solvable in polynomial time for every fixed a and d. Our results also extend to nonlinear separable convex objective functions. Funding: F. Eisenbrand, C. Hunkenschröder, and K.-M. Klein were supported by the Swiss National Science Foundation (SNSF) within the project “Convexity, geometry of numbers, and the complexity of integer programming” [Grant 163071]. A. Levin and S. Onn are partially supported by the Israel Science Foundation [Grant 308/18]. A. Levin is also partially supported by the Israel Science Foundation [Grant 1467/22]. S. Onn is also partially supported by the Dresner Chair at the Technion. M. Koutecký is partially supported by Charles University project UNCE 24/SCI/008, and by the project 22-22997S of the Grantová Agentura České Republiky (GA ČR).

Правовое регулирование возмещения вреда, причиненного преступлением в энергетической сфере

The article considers the issues of sufficiency of legal protection of the fuel and energy complex. Based on the method of comparative legal research and analysis of statistical information using formal logic, it was concluded that it is necessary to develop institutional foundations for compensation for harm from criminal acts to certain energy objects. It is proposed to single out the fuel and energy complex as a separate object of criminal law protection; clarify the procedural form of compensation for harm in the commission of crimes in the field of energy; at the local level, build and revise internal legal models in accordance with the risks of each individual company.

Posterior approach to correct for focal plane offsets in lattice light-sheet structured illumination microscopy.

Lattice light-sheet structured illumination microscopy (latticeSIM) has proven highly effective in producing three-dimensional images with super resolution rapidly and with minimal photobleaching. However, due to the use of two separate objectives, sample-induced aberrations can result in an offset between the planes of excitation and detection, causing artifacts in the reconstructed images. We introduce a posterior approach to detect and correct the axial offset between the excitation and detection focal planes in latticeSIM and provide a method to minimize artifacts in the reconstructed images. We utilized the residual phase information within the overlap regions of the laterally shifted structured illumination microscopy information components in frequency space to retrieve the axial offset between the excitation and the detection focal planes in latticeSIM. We validated our technique through simulations and experiments, encompassing a range of samples from fluorescent beads to subcellular structures of adherent cells. We also show that using transfer functions with the same axial offset as the one present during data acquisition results in reconstructed images with minimal artifacts and salvages otherwise unusable data. We envision that our method will be a valuable addition to restore image quality in latticeSIM datasets even for those acquired under non-ideal experimental conditions.

Frame-Based Change Detection Using Histogram and Threshold to Separate Moving Objects from Dynamic Background

Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.

Breaking Boundaries: A Universal Wavefront Reconstruction Approach for High-resolution Solar Imaging

This Letter proposes a universal wavefront reconstruction approach based on a coupled data set and neural network, aiming to overcome the limitations of current algorithms in terms of universality and wavefront sensing accuracy for variable imaging objects. First, a novel data set, Multi-Object Wavefront Coupling Dataset (MOCD-Dataset), is developed to provide diverse data and enable the network to learn universal wavefront features. Next, a new universal wavefront reconstruction network called Object-Independent Wavefront Decoupling Network (OIWD-Net) is introduced, aiming to separate imaging object information from multiple variable images. Our algorithm eliminates the need for specialized wavefront sensors, has a simple system, high light energy utilization, and does not require customized models for each different type of imaging objects, making it highly practical. By combining the MOCD-Dataset and the OIWD-Net, excellent accuracy in wavefront reconstruction of different imaging objects has been achieved. This research provides a new solution for high-resolution image restoration in fields such as solar structure observation and astronomical high-resolution imaging.

An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging

Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image. However, existing methods for image segmentation have their limitations when applied to certain types of images. This survey paper aims to highlight the importance of image segmentation techniques by providing a thorough examination of their advantages and disadvantages. The accurate detection of cancer regions in medical images is crucial for ensuring effective treatment. In this study, we have also extensive analysis of Computer-Aided Diagnosis (CAD) systems for cancer identification, with a focus on recent research advancements. The paper critically assesses various techniques for cancer detection and compares their effectiveness. Convolutional neural networks (CNNs) have attracted particular interest due to their ability to segment and classify medical images in large datasets, thanks to their capacity for self- learning and decision-making.

Multiethnic growth standards for fetal body composition and organ volumes derived from 3D ultrasonography

BackgroundA major goal of contemporary obstetrical practice is to optimize fetal growth and development throughout pregnancy. To date, fetal growth during prenatal care is assessed by performing ultrasonographic measurement of two-dimensional fetal biometry to calculate an estimated fetal weight. Our group previously established two-dimensional fetal growth standards using sonographic data from a large cohort with multiple sonograms. A separate objective of that investigation involved the collection of fetal volumes from the same cohort. ObjectiveThe Fetal 3D Study was designed to establish standards for fetal soft tissue and organ volume measurements by three-dimensional ultrasonography and compare growth trajectories with conventional two-dimensional measures where applicable. Study DesignThe NICHD Fetal 3D Study included research-quality images of singletons collected in a prospective, racially and ethnically diverse, low-risk cohort of pregnant individuals at 12 U.S. sites, with up to five scans per fetus (N=1,730 fetuses). Abdominal subcutaneous tissue thickness was measured from two-dimensional images and fetal limb soft tissue parameters extracted from three-dimensional multiplanar views. Cerebellar, lung, liver and kidney volumes were measured using virtual organ computer aided analysis (VOCAL). Fractional arm and thigh total volumes, and fractional lean limb volumes were measured, with fractional limb fat volume calculated by subtracting lean from total. For each measure, weighted curves (5th, 50th, 95th percentiles) were derived from 15-41 weeks’ using linear mixed models for repeated measures with cubic splines. ResultsSubcutaneous thickness of the abdomen, arm, and thigh increased linearly, with slight acceleration around 27-29 weeks. Fractional volumes of the arm, thigh, and lean limb volumes increased along a quadratic curvature, with acceleration around 29-30 weeks. In contrast, growth patterns for two-dimensional humerus and femur lengths demonstrated a logarithmic shape, with fastest growth in the 2nd trimester. The mid-arm area curve was similar in shape to fractional arm volume, with an acceleration around 30 weeks, whereas the curve for the lean arm area was more gradual. The abdominal area curve was similar to the mid-arm area curve with an acceleration around 29 weeks. The mid-thigh and lean area curves differed from the arm areas by exhibiting a deceleration at 39 weeks. The growth curves for the mid arm and thigh circumferences were more linear with some decelerations. Cerebellar two-dimensional diameter increased linearly, whereas cerebellar three-dimensional volume growth gradually accelerated until 32 weeks and then decelerated. Lung, kidney, and liver volumes all demonstrated gradual early growth followed by a linear acceleration beginning at 25 weeks for lungs, 26-27 weeks for kidneys, and 29 weeks for liver. ConclusionGrowth patterns and timing of maximal growth for three-dimensional lean and fat measures, limb and organ volumes differed from patterns revealed by traditional two-dimensional growth measures, suggesting these parameters reflect unique facets of fetal growth. Growth in these three-dimensional measures may be altered by genetic, nutritional, metabolic or environmental influences and pregnancy complications, in ways not identifiable using corresponding two-dimensional measures. Further investigation into the relationships of these three-dimensional standards to abnormal fetal growth, adverse perinatal outcomes, and health status in postnatal life is warranted.

NAKAGIN RESIDENTIAL TOWER IN TOKYO, JAPAN: A FUTURISTIC ARCHITECTURAL CONCEPT OF THE MID-20TH CENTURY AND REALITY

The work is devoted to a retrospective analysis of the historical and social conditions of the construction, existence and dismantling of the futuristic Nakagin capsule tower. The Nakagin Capsule Tower was built in 1970-1972 in central Tokyo by architect Kisho Kurokawa and was similar to the modernist building of the Shizuoka Press Center by architect Kenzo Tange. Both buildings appeared as a practical illustration of the concept of architectural metabolism, that is, the possibility of giving architecture the ability of living organisms to grow and renew themselves, adapting to the external environment. Initially, the building was intended for a hotel, but finally it received a mixed function of temporary and permanent accommodation. The structure of the tower consisted of a stationary reinforced concrete vertical core, on which residential capsules were suspended with the possibility of replacement. However, in reality, the Nakagin tower was unable to adapt to changing external conditions. During 50 years of operation, the capsules were never replaced, as the owners could not replace them at once, and the individual replacement was technically difficult. This led to the complete deterioration of the Nakagin Tower and its dismantling in 2022. Attempts to reconstruct the object did not receive adequate attention and sufficient funding. During the dismantling of the building, the public association of Nakagin Capsule Tower Preservation and Restoration Project managed to remove 23 capsules for their further repair with the aim of turning them into separate art objects and exhibition pavilions. Thus, it can be stated that the futuristic idea of the Nakagin residential tower, which was far ahead of its time during construction, did not stand the test of reality. Despite this, this project in many ways laid the foundations of modern concepts of dynamic architecture.

Evolution of grain size distribution in the circumgalactic medium

Abstract In order to theoretically understand dust properties in the circumgalactic medium (CGM), we construct a dust evolution model that incorporates the evolution of grain size distribution. We treat the galaxy and the CGM as separate one-zone objects, and consider the mass exchange between them. We take into account dust production and interstellar dust processing for the galaxy based on our previous models, and newly incorporate sputtering in the hot phase and shattering in the cool phase for the CGM. We find that shattering increases the dust destruction (sputtering) efficiency in the CGM. The functional shape of the grain size distribution in the CGM evolves following that in the galaxy, but it is sensitive to the balance between sputtering and shattering in the CGM. For an observational test, we discuss the wavelength dependence of the reddening in the CGM traced by background quasar colors, arguing that, in order to explain the observed reddening level, a rapid inflow from the CGM to the galaxy is favored because of quick dust/metal enrichment. Small grain production by shattering in the CGM also helps to explain the rise of dust extinction toward short wavelengths.

Separation density prediction of geldart A– dense medium in gas-solid fluidized bed coal beneficiators

Gas-solid Fluidized Bed Coal Beneficiator (GFBCB) process is a crucial dry coal beneficiation fluidization technology. The work employs the GFBCB process alongside a novel Geldart A– dense medium, consisting of Geldart A magnetite particles and Geldart C ultrafine coal, to separate small-size separated objects in the GFBCB. The effects of various operational conditions, including the volume fraction of ultrafine coal, the gas velocity, the separated objects size, and the separation time, were investigated on the GFBCB's separation performance. The results indicated that the probable error for 6∼3 mm separated objects could be controlled within 0.10 g/cm3. Compared to the traditional Geldart B/D dense medium, the Geldart A/A– dense medium exhibited better size-dependent separation performance with an overall probable error 0.04∼0.12 g/cm3. Moreover, it achieved a similar separation accuracy to the Geldart B/D dense medium fluidized bed with different external energy for the small-size object beneficiation. The work furthermore validated a separation density prediction model based on theoretical derivation, available for both Geldart B/D dense medium and Geldart A/A– dense medium at different operational conditions.

Teaching and Preaching

Abstract During European colonial times in Africa and elsewhere, missionary education was an integral part of the colonial instruments for political domination, economic exploitation, and cultural assimilation. This paper aims to investigate the process of making colonial subjects through missionary education that was mainly provided by Catholic and Evangelical mission schools during the Italian colonial period in Eritrea. The paper argues that the Catholic and Evangelical mission schools distinctively worked to achieve their separate objectives that can be explained as employment versus salvation, teaching versus preaching, flag versus Bible, and hands versus soul, respectively. While the Catholic mission school focused on training the hand in order to supply labour, the Evangelical mission school stressed harvesting the soul to cultivate a docile labour force. Despite their differences, the works of the Catholic and Evangelical mission schools placed much emphasis on and exerted much effort to producing a class of colonial subjects that could serve as brokers of power.

CEPB dataset: a photorealistic dataset to foster the research on bin picking in cluttered environments.

Several datasets have been proposed in the literature, focusing on object detection and pose estimation. The majority of them are interested in recognizing isolated objects or the pose of objects in well-organized scenarios. This work introduces a novel dataset that aims to stress vision algorithms in the difficult task of object detection and pose estimation in highly cluttered scenes concerning the specific case of bin picking for the Cluttered Environment Picking Benchmark (CEPB). The dataset provides about 1.5M virtually generated photo-realistic images (RGB + depth + normals + segmentation) of 50K annotated cluttered scenes mixing rigid, soft, and deformable objects of varying sizes used in existing robotic picking benchmarks together with their 3D models (40 objects). Such images include three different camera positions, three light conditions, and multiple High Dynamic Range Imaging (HDRI) maps for domain randomization purposes. The annotations contain the 2D and 3D bounding boxes of the involved objects, the centroids' poses (translation + quaternion), and the visibility percentage of the objects' surfaces. Nearly 10K separated object images are presented to perform simple tests and compare them with more complex cluttered scenarios tests. A baseline performed with the DOPE neural network is reported to highlight the challenges introduced by the novel dataset.