Mixed palletizing remains a core challenge in distribution centers and modern warehouse operations, particularly within robotic handling and automation systems. Progress in this domain has been hindered by the lack of realistic, freely available data...
Deep learning models are increasingly evaluated not only for predictive accuracy but also for their robustness, interpretability, and data quality dependencies. However, current benchmarks largely isolate these dimensions, lacking a unified evaluatio...
Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the val...
Object detection from waterborne imagery is an essential aspect in maritime traffic management, navigation safety and coastal security. Building efficient autonomous systems, which can take decisions in critical situations, requires an integration of...
Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, d...
The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding...
High-quality underwater images are essential for both human visual perception and machine analysis in marine vision applications. Although significant progress has been achieved in Underwater Image Quality Assessment (UIQA), almost all existing UIQA...
Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory h...
Registering UAV-based thermal and visible images is a challenging task due to differences in appearance across spectra and the lack of public benchmarks. To address this issue, we introduce UAV-TIRVis, a dataset consisting of 80 accurately and manual...
With the continuous advancement of the construction of smart cities, the availability of large-scale and semantically enriched datasets is essential for enhancing the machine’s ability to understand urban scenes. Mesh data have a distinct advan...
To promote the development of deep learning for feature matching, image registration, and three-dimensional reconstruction, we propose a method of constructing a deep learning benchmark dataset for affine-invariant feature matching. Existing images o...
Affective understanding of language is an important research focus in artificial intelligence. The large-scale annotated datasets of Chinese textual affective structure (CTAS) are the foundation for subsequent higher-level analysis of documents. Howe...
Epitope prediction based on random peptide library screening has become a focus as a promising method in immunoinformatics research. Some novel software and web-based servers have been proposed in recent years and have succeeded in given test cases....
The accurate reconstruction of indoor environments is crucial for applications in augmented reality, virtual reality, and robotics. However, existing indoor datasets are often limited in scale, lack ground truth point clouds, and provide insufficient...
Algorithm-level payload identification (determining the specific encryption algorithm, compression format, or encoding scheme from raw byte fragments) is a foundational capability for ransomware detection, digital forensics, and cryptographic complia...
Earth observation satellites offer vast opportunities for quantifying landscapes and regional land cover composition and changes. The integration of artificial intelligence in remote sensing is essential for monitoring significant land cover types li...
Camouflaged object detection (COD) is important for intelligent UAV monitoring and search-and-rescue operations. However, existing benchmarks focus primarily on natural camouflage, creating a noticeable domain shift for specific applications such as...
Accurate channel estimation is essential for reliable wireless communication, yet it becomes significantly challenging in 6G due to extreme propagation conditions. Factors such as high mobility, large delay spreads, and low signal-to-noise ratios (SN...
Offshore mariculture is critical for global food security and economic development. Advances in deep learning and data-driven approaches, enable the rapid and effective monitoring of offshore mariculture distribution and changes. However, detector pe...
Object detection in visible nearshore surveillance imagery is of great importance for maritime safety, intelligent coastal monitoring, and water rescue applications. Nevertheless, reliable detection remains difficult because nearshore scenes often co...
The expertly rectified extraction of cropland parcel boundaries is a critical basis for precision agriculture deployment and land resource management. However, the scarcity of large-scale, high-quality, cross-regional reference datasets limits the ge...
Background and Purpose: We present a pilot benchmark dataset of 378 preclinical histological samples for evaluating large language model (LLM) performance on multi-dimensional classification tasks. This dataset addresses the lack of standardized benc...
The problem of data enrichment to train visual SLAM and VO construction models using deep learning (DL) is an urgent problem today in computer vision. DL requires a large amount of data to train a model, and more data with many different contextual a...
Over the past few decades, researchers have shown sustained and robust investment in exploring methods for hyperspectral image classification (HSIC). The utilization of hyperspectral imagery (HSI) for crop classification in agricultural areas has bee...
Unmanned aerial vehicles (UAVs) play an increasingly vital role in wilderness search and rescue (SAR) operations by enhancing situational awareness and extending the capabilities of human teams. Yet, a lack of standardized benchmarks has impeded the...
The increasing complexity of steganographic techniques used to conceal information in digital content constitutes a major challenge for cybersecurity and digital forensics. Although deep learning has achieved strong performance in binary steganalysis...
With the increasing volume of collected Earth observation (EO) data, artificial intelligence (AI) methods have become state-of-the-art in processing and analyzing them. However, there is still a lack of high-quality, large-scale EO datasets for train...
Highly accurate and precise heave decay tests on a sphere with a diameter of 300 mm were completed in a meticulously designed test setup in the wave basin in the Ocean and Coastal Engineering Laboratory at Aalborg University, Denmark. The tests were...
Background/Objectives: Artificial intelligence (AI) models for glaucoma screening using colour fundus photography have shown strong internal performance; however, their external validity and calibration reliability remain uncertain. This study develo...
Modern intrusion detection systems (IDSs) are often evaluated under matched training and test conditions, whereas deployment environments involve changing traffic distributions, heterogeneous feature-generation pipelines, and shifting attack prevalen...
Wildfires need a fast response, and a small unmanned aerial vehicle (UAV) carrying its own detector is an appealing way to find them early. Almost every published UAV fire detector is validated on a single corpus, which leaves open the question that...
Time series analysis faces fundamental challenges when series contain trends, structural breaks, volatility, or anomalies that violate stationarity assumptions. Existing statistical tests produce inconsistent results on complex real-world patterns, w...
The anthropogenic climate crisis results in the gradual loss of tree species in locations where they were previously able to grow. This leads to increasing workloads and requirements for foresters and arborists as they are forced to restructure their...
Understanding spatial relationships between objects in images is crucial for robotic navigation, augmented reality systems, and autonomous driving applications, among others. However, existing vision-language benchmarks often overlook explicit spatia...
Background: The early detection of melanoma is crucial for improving patient outcomes, as survival rates decline dramatically with disease progression. Despite significant achievements in deep learning methods for skin lesion analysis, several challe...
Point cloud registration is the process of aligning point clouds collected at different locations of the same scene, which transforms the data into a common coordinate system and forms an integrated dataset. It is a fundamental task before the applic...
This work presents a reproducible YOLO-based pipeline for bottle detection in sandy environments, emphasizing dataset integrity, leakage-free evaluation, and deployment-oriented model selection. A one-class dataset of 1585 images and 3167 annotated b...
Despite the increasing use of machine learning (ML) and deep learning (DL) for leak detection and localization in water distribution networks (WDNs), standardized datasets, common evaluation protocols, and consistent reporting practices remain limite...
The safe management of flammable and explosive objects is critical for preventing industrial accidents. However, there is currently no publicly available dataset dedicated to their automated detection. To address this gap, we present EF-Data as the p...
Barren plateaus (BPs) pose a major trainability challenge for quantum neural networks (QNNs) by causing gradients to concentrate near zero as circuit size, depth, or expressibility increases. This study presents a comparative benchmark of 10 BP mitig...
Seed germination and early survival are important phenotypes for plant breeding and agricultural management, yet they are still commonly assessed through labor-intensive manual stand counting. We present the Maize Seedling Detection Dataset (MSDD), a...
Studies that use the same public lithium-ion battery dataset do not necessarily evaluate the same state-of-health (SOH) estimation task; reported performance depends on how the experiment is defined. We mapped 408 studies and then examined a nested s...
Power line inspection is an important part of the smart grid. Efficient real-time detection of power devices on the power line is a challenging problem for power line inspection. In recent years, deep learning methods have achieved remarkable results...
Earth Observation satellite imaging helps building diagnosis during a disaster. Several models are put forward on the xBD dataset, which can be divided into two levels: the building level and the pixel level. Models from two levels evolve into severa...
Datasets are essential to the development and evaluation of machine learning and artificial intelligence algorithms. As new tasks are addressed, new datasets are required. Training algorithms for human-aware navigation is an example of this need. Dif...
Metabolic pathways are a human-defined grouping of life sustaining biochemical reactions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unkno...
Candida auris is a multidrug-resistant pathogen that represents a serious public health threat due to its rapid global emergence, increasing incidence of healthcare-associated outbreaks, and high rates of antifungal resistance. Whole-genome sequencin...
Wood plate bark removal processing is critical for ensuring the quality of wood processing and its products. To address the issue of lack of datasets available for the application of deep learning methods to this field, and to fill the research gap o...
Background: Artificial intelligence (AI) has shown promise in breast ultrasound image analysis, but most evidence still comes from single-dataset studies. Clinical translation requires evaluation under heterogeneous acquisition and curation condition...
(1) Background: the ability to use social media to communicate without revealing one’s real identity has created an attractive setting for cyberbullying. Several studies targeted social media to collect their datasets with the aim of automatica...
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