This research presents an advanced YOLOv8-MMD framework specifically designed for intelligent white radish harvesting systems, addressing the critical need for simultaneous species recognition and quality evaluation. The proposed architecture is buil...
The main challenges of semantic segmentation in vehicle-mounted scenes are object scale variation and trading off model accuracy and efficiency. Lightweight backbone networks for semantic segmentation usually extract single-scale features layer-by-la...
Automated building footprint extraction requires the Deep Learning (DL)-based semantic segmentation of high-resolution Earth observation images. Fully convolutional networks (FCNs) such as U-Net and ResUNET are widely used for such segmentation. The...
Accurate identification of the combustion state in municipal solid waste incineration (MSWI) processes is crucial for achieving efficient, low-emission, and safe operation. However, existing methods often struggle with stable and reliable recognition...
Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-Feature Pyramid Network, a novel one-stage 3D object detector that utilizes r...
We present photometric stereo algorithms robust to non-Lambertian reflection, which are based on a convolutional neural network in which surface normals of objects with complex geometry and surface reflectance are estimated from a given set of an arb...
Water area segmentation is an important branch of remote sensing image segmentation, but in reality, most water area images have complex and diverse backgrounds. Traditional detection methods cannot accurately identify small tributaries due to incomp...
The effective use of multi-scale features remains an open problem for object detection tasks. Recently, proposed object detectors have usually used Feature Pyramid Networks (FPN) to fuse multi-scale features. Since Feature Pyramid Networks use a rela...
This paper proposes a multiplexing multi-scale features network (MMF-Network) for salient target detection to tackle the issue of incomplete detection structures when identifying salient targets across different scales. The network, based on encoder&...
Land cover semantic segmentation is an important technique in land. It is very practical in land resource protection planning, geographical classification, surveying and mapping analysis. Deep learning shows excellent performance in picture segmentat...
Amidst the dual challenges of energy shortages and global warming, photovoltaic (PV) power generation has emerged as a critical technology due to its efficient utilization of solar energy. Rooftops, as underutilized spaces, are ideal locations for in...
The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the imp...
In this paper, our goal is to improve the recognition accuracy of battlefield target aggregation behavior while maintaining the low computational cost of spatio-temporal depth neural networks. To this end, we propose a novel 3D-CNN (3D Convolutional...
Conflicts between humans and animals in agricultural and settlement areas have recently increased, resulting in significant resource loss and risks to human and animal lives. This growing issue presents a global challenge. This paper addresses the de...
The effective segmentation of clouds and cloud shadows is crucial for surface feature extraction, climate monitoring, and atmospheric correction, but it remains a critical challenge in remote sensing image processing. Cloud features are intricate, wi...
Accurate and effective classification of lidar point clouds with discriminative features expression is a challenging task for scene understanding. In order to improve the accuracy and the robustness of point cloud classification based on single point...
Unmanned aerial vehicle (UAV) image object detection has great application value in the military and civilian fields. However, the objects in the captured images from UAVs have problems of large-scale variation, complex backgrounds, and a large propo...
Synthetic aperture radar images have all-weather and all-time capabilities and are widely used in the field of ship target surveillance at sea. However, its detection accuracy is often limited by factors such as complex sea conditions, diverse ship s...
With the development of deep learning, significant improvements and optimizations have been made in salient object detection. However, many salient object detection methods have limitations, such as insufficient context information extraction, limite...
In recent years, with the extensive application of deep learning in images, the task of remote sensing image change detection has witnessed a significant improvement. Several excellent methods based on Convolutional Neural Networks and emerging trans...
As a core task in remote sensing image processing, change detection plays a vital role in dynamic surface monitoring for environmental management, urban planning, and agricultural supervision. However, existing methods often suffer from missed detect...
To tackle the challenges of weak sensing capacity for multi-scale objects, high missed detection rates for occluded targets, and difficulties for model deployment in detection tasks of intelligent roadside perception systems, the PDT-YOLO algorithm b...
Underwater image enhancement (UIE) is a key technology in the fields of underwater robot navigation, marine resources development, and ecological environment monitoring. Due to the absorption and scattering of different wavelengths of light in water,...
Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on pa...
Accurate and efficient detection of floating waste is crucial for environmental protection and aquatic ecosystem preservation, yet remains challenging due to environmental interference and the prevalence of small targets. To address these limitations...
Semantic change detection has become a key technology for monitoring the evolution of land cover and land use categories at the semantic level. However, existing methods often lack effective information interaction and fail to capture changes at mult...
Single image super-resolution (SISR) is a classical computer vision task that aims to reconstruct a high-resolution image from a low-resolution input, thereby improving detail sharpness and visual quality. In recent years, convolutional neural networ...
Cloud detection constitutes a pivotal task in remote sensing preprocessing, yet detecting cloud boundaries and identifying thin clouds under complex scenarios remain formidable challenges. In response to this challenge, we designed a network model, n...
With the rapid development of deep learning, object detection in remote sensing images has attracted extensive attention. However, remote sensing images typically exhibit the following characteristics: significant variations in object scales, dense s...
Semantic change detection technology based on remote sensing data holds significant importance for urban and rural planning decisions and the monitoring of ground objects. However, simple convolutional networks are limited by the receptive field, can...
This research introduces the Enhanced Scale-Aware efficient Transformer (ESAE-Transformer), a novel and advanced model dedicated to predicting Exhaust Gas Temperature (EGT). The ESAE-Transformer merges the Multi-Head ProbSparse Attention mechanism wi...
High-precision measurement of temperature value distributions in production scenarios is of great significance for industrial production, but traditional temperature field reconstruction algorithms rely on the design of manual feature extraction meth...
Micro-X-ray fluorescence spectroscopy (micro-XRF) integrates spatial and spectral information and is widely employed for multi-elemental analyses of rock-forming minerals. However, its inherent limitation in spatial resolution gives rise to significa...
The integrity and stability of railway fasteners are of vital importance to railway safety. To address the challenges of limited anomaly samples, irregular defect geometries, and complex operational conditions in rail fastener anomaly detection, this...
To mitigate the reduced accuracy of direction-of-arrival (DOA) estimation in scenarios with low signal-to-noise ratios (SNR) and multiple interfering sources, this paper proposes an Auxiliary Classifier Generative Adversarial Network (ACGAN) architec...
This paper introduces a novel approach to pavement material crack detection, classification, and segmentation using advanced deep learning techniques, including multi-scale feature aggregation and transformer-based attention mechanisms. The proposed...
At present, a prevalent approach to speaker diarization is clustering based on speaker embeddings. However, this method encounters two primary issues. Firstly, it cannot directly minimize the diarization error during the training process; secondly, t...
Underground utility tunnels face corrosion, cracks, and leakage after long-term use, endangering urban safety. Traditional methods have strong subjectivity, high miss rates, and poor real-time performance, failing refined management needs. This paper...
Hyperspectral remote sensing images, with their continuous, narrow, and rich spectra, hold distinct significance in the precise classification of land cover. Deep convolutional neural networks (CNNs) and their variants are increasingly utilized for h...
Referring Remote Sensing Image Segmentation (RRSIS) aims to accurately locate and segment target objects in high-resolution aerial imagery based on natural language descriptions. Most existing approaches either directly modify Referring Image Segment...
Lightweight Single-Image Super-Resolution (SISR) faces the core challenge of balancing computational efficiency with reconstruction quality, particularly in preserving both high-frequency details and global structures under constrained resources. To...
Employing the multi-scale strategy in hyperspectral image (HSI) classification enables the exploration of complex land-cover structures with diverse shapes. However, existing multi-scale methods still have limitations for fine feature extraction and...
In agricultural pest detection, the small size of pests poses a critical hurdle to detection accuracy. To mitigate this concern, we propose a Lightweight Cross-Level Feature Aggregation Network (LCFANet), which comprises three key components: a deep...
An accurate and reliable extraction of building structures from high-resolution (HR) remote sensing images is an important research topic in 3D cartography and smart city construction. However, despite the strong overall performance of recent deep le...
The development of Facial Expression Recognition (FER) technology has significantly enhanced the naturalness and intuitiveness of human-robot interaction. In the field of service robots, particularly in applications such as production assistance, car...
Deep learning approaches have been widely used in building automatic extraction tasks and have made great progress in recent years. However, the missing detection and wrong detection causing by spectrum confusion is still a great challenge. The exist...
Video super-resolution aims to generate high-resolution video sequences with realistic details from existing low-resolution video sequences. However, most existing video super-resolution models require substantial computational power and are not suit...
In microscopic imaging, the key to obtaining a fully clear image lies in effectively extracting and fusing the sharp regions from different focal planes. However, traditional multi-focus image fusion algorithms have high computational complexity, mak...
Background: The development of laser measurement techniques is of great significance in forestry monitoring and park management in smart cities. It provides many conveniences for improving landscape planning efficiency and strengthening digital const...
Lane detection, as a basic environmental perception task, plays a significant role in the safety of automatic driving. Modern lane detection methods have obtained a better performance in most scenarios, but many are unsatisfactory in various scenario...
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