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...
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...
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...
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...
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...
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....
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
Most mainstream research on assessing building damage using satellite imagery is based on scattered datasets and lacks unified standards and methods to quantify and compare the performance of different models. To mitigate these problems, the present...
Wearing a safety helmet is important in construction and manufacturing industrial activities to avoid unpleasant situations. This safety compliance can be ensured by developing an automatic helmet detection system using various computer vision and de...
Recognizing and classifying traffic signs is a challenging task that can significantly improve road safety. Deep neural networks have achieved impressive results in various applications, including object identification and automatic recognition of tr...
Recent advancement in Deep Learning-based Convolutional Neural Networks (D-CNNs) has led research to improve the efficiency and performance of barcode recognition in Supply Chain Management (SCM). D-CNNs required real-world images embedded with groun...
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...
Security vulnerabilities in IoT firmware pose significant risks, and black-box protocol fuzzing offers a scalable and cost-effective solution for identifying these vulnerabilities. However, current fuzzing evaluation schemes are often beset by challe...
Corners are an important kind of image feature and play a crucial role in solving various tasks. Over the past few decades, a great number of corner detectors have been proposed. However, there is no benchmark dataset with labeled ground-truth corner...
Inertial measurement units (IMUs) enable orientation, velocity, and position estimation in several application domains ranging from robotics and autonomous vehicles to human motion capture and rehabilitation engineering. Errors in orientation estimat...
In this article, the Laserscanner Multi-Fisheye Camera Dataset (LaFiDa) for applying benchmarks is presented. A head-mounted multi-fisheye camera system combined with a mobile laserscanner was utilized to capture the benchmark datasets. Besides this,...
Reproducibility is a cornerstone of scientific progress, yet in X-ray computed tomography (CT) reconstruction, it remains a critical and unresolved challenge. Current benchmarking practices in CT are hampered by the scarcity of openly available datas...
This article presents a dataset and experimental evaluation of a parallelized variant of Junker’s QuickXPlain algorithm, designed to efficiently compute minimal conflict sets in constraint-based diagnosis tasks. The dataset includes performance...
Accurate flood detection is critical for disaster response, yet the scarcity of diverse annotated datasets hinders robust model development. Existing resources typically suffer from limited geographic scope and insufficient annotation granularity, re...
Human detection is the task of locating all instances of human beings present in an image, which has a wide range of applications across various fields, including search and rescue, surveillance, and autonomous driving. The rapid advancement of compu...
Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representi...
Text-to-SQL leverages large language models (LLMs) for natural language database queries, yet existing benchmarks like BIRD (12,751 question–SQL pairs, 95 databases) suffer from inconsistencies—e.g., 30% of queries misalign with SQL outpu...
In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic–angular rate–gravity (MARG) sensor module (also known as magnet...
Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning...
Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this...
Multi-person pose forecasting involves predicting the future body poses of multiple individuals over time, involving complex movement dynamics and interaction dependencies. Its relevance spans various fields, including computer vision, robotics, huma...
A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as tempo...
Network intrusion detection is one of the main problems in ensuring the security of modern computer networks, Wireless Sensor Networks (WSN), and the Internet-of-Things (IoT). In order to develop efficient network-intrusion-detection methods, realist...
Research on eye detection and segmentation is even more important with mask-wearing measures implemented during the COVID-19 pandemic. Thus, it is necessary to build an eye image detection and segmentation dataset (EIMDSD), including labels for detec...
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...
Visual simultaneous localization and mapping (Visual SLAM) based on RGB-D image data includes two main tasks: One is to build an environment map, and the other is to simultaneously track the position and movement of visual odometry estimation (VOE)....
The thalamus is a subcortical brain structure linked to the motor system. Since certain changes within this structure are related to diseases, such as multiple sclerosis and Parkinson’s, the characterization of the thalamus—e.g., shape as...
The proliferation of Unmanned Aerial Vehicles (UAVs) equipped with multispectral imaging sensors has revolutionized data collection in precision agriculture. These platforms provide high-resolution, temporally dense data crucial for monitoring crop h...
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