Identifying cancer driver genes is fundamental for understanding tumor initiation and guiding therapeutic strategies. However, most existing methods assess gene importance from a global or static perspective, overlooking sample-specific functional di...
Factory extraction from satellite images is a key step in urban factory planning, and plays a crucial role in ecological protection and land-use optimization. However, factory extraction is greatly underexplored in the existing literature due to the...
Forest fires are destructive and rapidly spreading, causing great harm to forest ecosystems and humans. Deep learning techniques can adaptively learn and extract features of forest fires and smoke. However, the complex backgrounds and different fores...
Small-object representations in unmanned aerial vehicle (UAV) imagery are easily degraded by repeated downsampling, crowded occlusion, and background clutter. We present MF-YOLO, an accuracy-oriented detector based on YOLO11n. It introduces a stride-...
Accurate identification of maize seedlings and weeds at the seedling stage in dry farmland of cold regions serves as an essential prerequisite for field variable weeding and intelligent plant protection operations. Existing YOLO networks exhibit prom...
Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our fea...
Online rumors spread rapidly through social media, which is a great threat to public safety. Existing solutions are mainly based on content features or propagation structures for rumor detection. However, due to the variety of strategies in creating...
Colorectal cancer remains a major cause of cancer-related mortality, and missed adenomas during colonoscopy are still a persistent clinical concern. Video-based computer-aided diagnosis can support polyp recognition, but real colonoscopy clips often...
Conventional single-stage object detectors have been able to efficiently detect objects of various sizes using a feature pyramid network. However, because they adopt a too simple manner of aggregating feature maps, they cannot avoid performance degra...
Tomato leaf diseases pose significant threats to crop yield and food security. However, in real-world cultivation environments, factors such as fluctuating illumination, varying leaf occlusion, and ambiguous lesion morphology often compromise detecti...
Maritime ship detection faces challenges due to complex object poses, variable target scales, and background interference. This paper introduces YOLO-PFA, a novel SAR ship detection model that integrates multi-scale feature fusion and dynamic alignme...
Remaining Useful Life (RUL) prediction for aircraft engines is a critical task in prognostics and health management, aiming to extract degradation information from multi-sensor operational data to support maintenance scheduling and operational risk m...
Load disaggregation determines appliance-level energy consumption unintrusively from aggregated consumption measured by a single meter. Deep neural networks have been proven to have great potential in load disaggregation. In this article, a temporal...
Tomatoes are highly vulnerable to a wide range of leaf diseases, which significantly reduce agricultural yield and quality. Timely and precise detection of these diseases is essential for sustainable crop management and food security. This study anal...
To address the challenges of low detection accuracy and weak generalization in UAV aerial imagery caused by complex ground environments, significant scale variations among targets, dense small objects, and background interference, this paper proposes...
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...
In aircraft detection from synthetic aperture radar (SAR) images, there are several major challenges: the shattered features of the aircraft, the size heterogeneity and the interference of a complex background. To address these problems, an Efficient...
In this paper, an object detection and recognition method based on improved YOLOv5 is proposed for application on unmanned aerial vehicle (UAV) aerial images. Firstly, we improved the traditional Gabor function to obtain Gabor convolutional kernels w...
The structural application of Recycled Aggregate Concrete (RAC) in marine and coastal structures remains restricted by its highly variable quality and uncertain durability. Although the adhered old mortar is recognized as the most distinctive feature...
Hyperspectral image (HSI) classification aims to assign a land cover label to each pixel by jointly exploiting spectral signatures and spatial contextual information. Recently, Mamba-based state-space models have shown potential for long-range depend...
Highway road debris is often small, irregular, weakly textured, and poorly contrasted against complex backgrounds, while limited real-world samples constrain model generalization. This study constructs a Synthetic-Debris dataset by compositing three...
This study proposes a novel lightweight grape detection method. First, the backbone network of our method is Uniformer, which captures long-range dependencies and further improves the feature extraction capability. Then, a Bi-directional Path Aggrega...
To address the problem of low efficiency for manual detection in the defect detection field for metal shafts, we propose a deep learning defect detection method based on the improved YOLOv5 algorithm. First, we add a Convolutional Block Attention Mod...
HVAC (Heating, Ventilation and Air Conditioning) system in buildings is a major component of energy consumption, and realizing high-precision energy consumption prediction is of great significance for intelligent building management. Aiming at the pr...
Non-cooperative orthogonal frequency division multiplexing (OFDM) systems over time-varying channels suffer from inter-carrier interference (ICI), deep fading, carrier frequency offset (CFO), and phase offset (PO), which severely degrade conventional...
Limited labeled data and overlapping multisensor signatures make liquid rocket engine fault classification challenging, particularly when feature fusion introduces redundant information. This study proposes a multi-scale feature fusion framework with...
The surface defect detection of printed circuit boards (PCBs) plays a crucial role in the field of industrial manufacturing. However, the existing PCB defect detection methods have great challenges in detecting the accuracy of tiny defects under the...
Traffic safety is a significant global concern, and traffic sign recognition (TSR) is essential for the advancement of intelligent transportation systems. Traditional YOLO11s-based methods often struggle to balance detection accuracy and processing s...
To address challenges in the detection of wind turbine blade damage images, characterized by complex backgrounds and multiscale feature distribution, we propose a method based on an enhanced YOLOV8 model. Our approach focuses on three key aspects: Fi...
Reliable three-dimensional (3D) object detection is critical for intelligent vehicles to ensure safety in complex traffic environments, and recent progress in multi-modal sensor fusion, particularly between LiDAR and camera, has advanced environment...
Federated learning enables multiple devices to collaboratively train a high-performance model on the central server while keeping their data on the devices themselves. However, due to the significant variability in data distribution across devices, t...
Multimodal sentiment analysis (MSA) has become a key research area in artificial intelligence, aiming to predict sentiment polarity or intensity by jointly modeling text, audio, and visual information. However, traditional methods still face signific...
Hyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness i...
Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant charact...
Industrial metal surface defect detection is essential for visual sensing-based quality inspection, where lightweight models must balance reliable defect perception and efficient deployment under limited computational resources. However, weak texture...
Small-object detection in unmanned aerial vehicle (UAV) images poses significant challenges due to limited pixel representation, complex backgrounds, and insufficient feature discriminability. While one-stage detectors like YOLO offer a favorable spe...
Insulators are critical insulation components in power transmission lines; however long-term exposure to adverse environmental conditions may threaten the safety and stability of power delivery. Existing studies primarily emphasize detection accuracy...
To effectively monitor the nonlinear wear variation of tools during the processing of titanium alloys, this study proposes a hybrid deep neural network fault diagnosis model that integrates the triangulation topology aggregation optimizer (TTAO), con...
Short-term load forecasting (STLF), especially for regional aggregate load forecasting, is essential in smart grid operation and control. However, the existing CNN-based methods cannot efficiently extract the essential features from the electricity l...
Unmanned Aerial Vehicle (UAV) vehicle-tracking technology has extensive potential for application in various fields. In the actual tracking process, the relative movement of the UAV and vehicles will bring large target-scale variations (i.e., size an...
Transmission-line inspection requires a single detector to localize complete insulator strings together with much smaller broken-shell and flashover-damaged regions. These targets differ in spatial extent and in their dependence on local detail and s...
Among the key components of a smart grid, advanced metering infrastructure (AMI) has become the preferred target for network intrusion due to its bidirectional communication and Internet connection. Intrusion detection systems (IDSs) can monitor abno...
Due to changes in light intensity, varying degrees of aphid aggregation, and small scales in the climate chamber environment, accurately identifying and counting aphids remains a challenge. In this paper, an improved YOLOv5 aphid detection model base...
A YOLOv5 aluminum profile defect detection algorithm that integrates attention and multi-scale features is proposed in this paper to address the issues of the low detection accuracy, high false detection rates, and high missed detection rates that ar...
Special target detection in UAV remote sensing imagery is challenged by composite multi-type degradation, which collectively erodes target structure across every stage of a detection pipeline. Existing methods address individual degradation types in...
In attribute-based multimodal knowledge graphs, images and text serve as auxiliary entity attributes, providing semantic evidence beyond graph topology for link prediction. However, bidirectional conditional dependencies between an entity’s mul...
Encrypted traffic classification is essential for network management and security, yet payload inspection is ineffective under modern protocols such as Transport Layer Security (TLS) and Quick UDP Internet Connections (QUIC). Existing metadata-based...
The rapid evolution of deepfake generation techniques has made high-fidelity facial manipulation a critical threat to social credibility and personal privacy, demanding detection algorithms with strong cross-domain generalization. Existing methods su...
Multi-modality three-dimensional (3D) object detection is a crucial technology for the safe and effective operation of environment perception systems in autonomous driving. In this study, we propose a method called context clustering-based radar and...
Accurately predicting the reception area of the Automatic Identification System (AIS) is critical for ship tracking and anomaly detection, as errors in signal interpretation may lead to incorrect vessel localization and behavior analysis. However, tr...
of 4