The dataset is a structured image dataset designed to facilitate spatial localization, pattern recognition, and character classification research. It contains high-resolution images of 53 distinct alphabet characters, each systematically placed within a standardized 5×5 grid layout.
This archive contains image files showing X-rays of a suitcase with three dangerous objects, such as a knife, a revolver, and a grenade. The images are made in four different color representations. The images with labels are intended for training the YOLO network. The collection is divided into training sets and test sets. The test sets contain images contaminated with impulsive noise.
With the introduction of the low-altitude economy concept, the application of electric vertical takeoff and landing (eVTOL) aircraft has become more widespread, particularly in search and rescue missions. However, most of the existing path planning methods cannot effectively cope with dynamic environments and changes in destinations, which limits the ability of eVTOL drones to autonomously perform planning tasks in unknown dynamic environments.
ThermalTrack is an RGB-LWIR paired dataset of wheel tracks captured under harsh winter conditions, including white-outs (severely degraded visibility), low-contrast snow terrain, and diverse wheel track geometries. Designed to enable robust alternative navigation strategies for winter autonomy systems, this dataset builds upon WADS (https://digitalcommons.mtu.edu/wads/), a specialized dataset for autonomous vehicle research in inclement winter weather.
High-quality annotated datasets from diverse scenarios play a crucial role in the development of deep learning algorithms. However, due to the strict access limitations of space-based infrared satellite platforms, space-based infrared small target datasets are scarce. Therefore, we have developed the MIRSat-QL dataset, based on a space-based infrared satellite platform, for space-based dynamic scene infrared target detection. Our data is synthesized from space-based infrared satellite images and ground-based infrared cameras capturing airborne targets. The specifics are as follows。
High-quality annotated datasets from diverse scenarios play a crucial role in the development of deep learning algorithms. However, due to the strict access limitations of space-based infrared satellite platforms, space-based infrared small target datasets are scarce. Therefore, we have developed the MIRSat-QL dataset, based on a space-based infrared satellite platform, for space-based dynamic scene infrared target detection. Our data is synthesized from space-based infrared satellite images and ground-based infrared cameras capturing airborne targets. The specifics are as follows。
During the course of this experimental study, we meticulously collected and recorded a comprehensive set of data. These data not only reflect the precise outcomes of the experimental procedures but also directly correspond to the contents presented in the tables within the research paper. These results are crucial for validating our research hypotheses, providing a solid quantitative foundation for our understanding and analysis of the experimental phenomena.
The example involves 16 evaluation criteria, with quantitative criteria including on time delivery (C1), delivery speed (C2), accurate delivery (C3), damaged cargo proportion (C4), after-sale service (C5), clearance efficiency (C6), geographical coverage (C7), bonded warehouse support (C8), delivery price (C12), and transport cost (C13), and qualitative criteria including flexibility in delivery and operations (C9), information system (C10), information sharing (C11), reputation (C14), financial performance (C15), and R&D ability (C16).
Benchmark code, HPC runtime logs, and analysis for the "AcceleratedKernels.jl: Cross-Architecture Parallel Algorithms from a Unified, Transpiled Codebase" Paper.
A dataset used in the paper "A Causal Perspective of Stock Prediction Models". The dataset is constructed using the training infromation between 2011 and 2024 via signals provided by GUOTAI JUNAN SECURITIES. Alpha191 is a widely used collection of 191 mathematical formulas, known as "alpha factors," used for quantitative stock analysis. Developed by researchers and practitioners, these factors are designed to capture various statistical properties, behavioral patterns, and market trends from financial data.
