With a large number of distributed generators (DGs) and electrical vehicles (EVs) integrated into the power distribution system, the complexity of distribution system operation is increased, which arises to higher requirements for online reactive pow...
This study addresses the prevalent challenges of inefficiency and suboptimal quality in indoor 3D scene generation and rendering by proposing a parameter-tuning strategy for 3D Gaussian Splatting (3DGS). Through a systematic quantitative analysis of...
In complex inshore regions, ships are often densely berthed and arbitrarily oriented, while docks, shorelines, coastal facilities, and strong scatterers produce substantial background clutter and target-like scattering responses, leading to foregroun...
Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in s...
Because mobile virtual reality (VR) is both mobile and immersive, three-dimensional (3D) visualizations of disaster scenes based in mobile VR enable users to perceive and recognize disaster environments faster and better than is possible with other m...
The rapid acquisition of deposit volume information and dynamic modeling, as well as the visualization of disaster scenes, have great significance for the sharing of landslide information and the management of emergency rescue. However, existing meth...
Regular geometric targets under microscopic scenes, such as microspheres, micropores, and microtubes, are characterized by small scales, low contrast, and degraded boundaries. Masks generated by general segmentation methods often fail to directly sup...
Neural rendering techniques aim to generate photorealistic images and accurate 3D geometries from multi-view images but often struggle with efficiency and geometric consistency in complex or dynamic scenes. Optimized Neural Radiance Fields (OptiNeRF)...
Car-like mobile robots (CLMRs) are extensively utilized in various intricate scenarios owing to their exceptional maneuverability, stability, and adaptability, in which path planning is an important technical basis for their autonomous navigation. Ho...
The robust and high-precision estimation of position and attitude information using a combined global navigation satellite system/inertial navigation system (GNSS/INS) model is essential to a wide range of applications in intelligent driving and smar...
Designing an acquisition system for 2D or 3D information, based on the integration of data provided by different sensors is a task that requires a labor-intensive initial design phase. Indeed, the definition of the architecture of such acquisition sy...
Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lea...
Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved...
Indoor mobile robots can be localized by using scene classification methods. Recently, two-dimensional (2D) LiDAR has achieved good results in semantic classification with target categories such as room and corridor. However, it is difficult to achie...
Energy storage (ES) only contributes to a single-scene (peak or frequency modulation (FM)) control of the power grid, resulting in low utilization rate and high economic cost. Herein, a coordinated control method of peak modulation and FM based on th...
The performance of deep learning is heavily influenced by the size of the learning samples, whose labeling process is time consuming and laborious. Deep learning algorithms typically assume that the training and prediction data are independent and un...
Protecting target regions from reconnaissance is a critical task in synthetic aperture radar (SAR) countermeasures. For large facilities such as airports and key infrastructure, deceptive scene jamming requires more than inserting isolated false targ...
The reconstruction of realistic large-scale 3D scene models using aerial images or videos has significant applications in smart cities, surveying and mapping, the military and other fields. In the current state-of-the-art 3D-reconstruction pipeline,...
Narrative mapping, an advanced geographic information visualization technology, presents spatial information episodically, enhancing readers’ spatial understanding and event cognition. However, during 3D scene construction, viewpoint selection...
Hydrogen has the physical and chemical characteristics of being flammable, explosive and prone to leakage, and its safety is the main issue faced by the promotion of hydrogen as an energy source. The most common scene in vehicle application is the lo...
Panoramic object detection is constrained at three levels of the detection pipeline: equirectangular projection deforms the input representation, crowded scenes weaken feature discrimination and complicate overlapping predictions, and high-resolution...
An integrated energy system interconnects multiple energies and presents a potential for economics improvement and energy sustainability, which has attracted extensive attention. However, due to the obvious volatility of energy demands, most existing...
Metro station areas are transitioning from single-purpose transportation transfer nodes into multifunctional public spaces that integrate public services, cultural displays, and place-based experiences. However, there remains a lack of systematic eva...
High-precision urban vegetation mapping is essential for assessing carbon sink capacities, mitigating the urban heat island effect, and supporting sustainable development. Although deep learning and high-resolution remote sensing have advanced automa...
The field of object detection has widespread applicability in many areas. Despite the multitude of object detection methods that are already established, complex scenes with occlusions still prove challenging due to the loss of information and dynami...
Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID...
Generalized stereo matching faces the radiation difference and small ground feature difference brought by different satellites and different time phases, while the texture-less and disparity discontinuity phenomenon seriously affects the corresponden...
We investigate the Optimal Obstacle Placement (OOP) problem under uncertainty, framed as the dual of the Optimal Traversal Path problem in the Stochastic Obstacle Scene paradigm. We consider both continuous domains, discretized for analysis, and alre...
This paper is focused mainly on the production simulation method of a regional integrated energy system under random scenarios for optimal configuration. First, the cooling, heating, and electric load demand of the regional integrated energy system i...
This article addresses the semantic segmentation of laparoscopic surgery images, placing special emphasis on the segmentation of structures with a smaller number of observations. As a result of this study, adjustment parameters are proposed for deep...
Surveillance video has emerged as a crucial data source for web Geographic Information Systems (GIS), playing a vital role in traffic management, facility monitoring, and anti-terrorism inspections. However, previous methods encountered significant c...
SLAM (Simultaneous Localization and Mapping), as one of the basic functions of mobile robots, has become a hot topic in the field of robotics this year. The majority of SLAM systems in use today, however, disregard the impact of dynamic objects on th...
With object detection technology, real-time detection of dense scenes has become an important application requirement in various industries, which is of great significance for improving production efficiency and ensuring public safety. However, the c...
Rotated ship detection in complex synthetic aperture radar (SAR) scenes remains a critical yet challenging task for maritime remote sensing applications. Existing methods are plagued by three core bottlenecks: inconsistent directional responses acros...
It is quite challenging to stitch images with continuous depth changes and complex textures. To solve this problem, we propose an optimized seam-driven image stitching method considering depth, color, and texture information of the scene. Specificall...
Simultaneous localization and mapping (SLAM) is one of the core technologies for intelligent mobile robots. However, when robots perform VSLAM in dynamic scenes, dynamic objects can reduce the accuracy of mapping and localization. If deep learning-ba...
The narrow corridor is a common working scene for automated vehicles, where it is pretty challenging to plan a safe, feasible, and smooth trajectory due to the narrow passable area constraints. This paper presents a space discretization-based optimal...
High-precision lunar scene 3D data are essential for lunar exploration and the construction of scientific research stations. Currently, most existing data from orbital imagery offers resolutions up to 0.5–2 m, which is inadequate for tasks requ...
Cylindrical panorama stitching is able to generate high resolution images of a scene with a wide field-of-view (FOV), making it a useful scene representation for applications like environmental sensing and robot localization. Traditional image stitch...
With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source...
In the realm of few-shot classification learning, the judicious application of data augmentation methods has a significantly positive impact on classification performance. In the context of few-shot classification tasks for remote sensing images, the...
In this paper, we revisit the paired image-to-image translation using the conditional generative adversarial network, the so-called “Pix2Pix”, and propose efficient optimization techniques for the architecture and the training method to m...
In off-road scenes, segmentation targets exhibit significant scale progression due to perspective depth effects from oblique viewing angles, meaning that the size of the same target undergoes continuous, boundary-less progressive changes along a spec...
In order to solve the risk of transient voltage instability caused by the increasing proportion of new energy represented by photovoltaic (PV) and dynamic load in the power grid, a dynamic reactive power compensation device configuration method with...
In the context of carbon peaking and carbon neutralization, distributed photovoltaics is a relatively mature new energy power generation technology that is being widely promoted. However, the randomness and volatility of distributed generation bring...
As the use of camera networks has expanded, camera placement to satisfy some quality assurance parameters (such as a good coverage ratio, an acceptable resolution constraints, an acceptable cost as low as possible, etc.) has become an important probl...
The complexity of urban road scenes at night and the inadequacy of visible light imaging in such conditions pose significant challenges. To address the issues of insufficient color information, texture detail, and low spatial resolution in infrared i...
Most visual simultaneous localization and mapping (SLAM) systems are based on the assumption of a static environment in autonomous vehicles. However, when dynamic objects, particularly vehicles, occupy a large portion of the image, the localization a...
To tackle the problems of high scheduling costs and low photovoltaic (PV) accommodation rates in port microgrids, which are caused by the coupling of uncertainties in new energy output and load randomness, this paper proposes an optimized scheduling...
Integrating diverse image datasets acquired from different satellites is challenging. Converting images from one sensor to another, like from WorldView-3 (WV) to SuperDove (SD), involves both changing image channel wavelengths and per-band intensity...
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