A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in...
Named entity recognition involves two main types: nested named entity recognition and flat named entity recognition. The span-based approach treats nested entities and flat entities uniformly by classifying entities on a span representation. However,...
With the recent rise in violent crime, the real-time situation analysis capabilities of the prevalent closed-circuit television have been employed for the deterrence and resolution of criminal activities. Anomaly detection can identify abnormal insta...
Electric motors play a crucial role in self-driving vehicles. Therefore, fault diagnosis in motors is important for ensuring the safety and reliability of vehicles. In order to improve fault detection performance, this paper proposes a motor fault di...
Gaussian derivatives offer valuable capabilities for analyzing image characteristics such as structure, edges, texture, and features, which are essential aspects in the assessment of image quality. Recently, Convolutional Neural Networks (CNNs) have...
Accurate fault diagnosis remains a critical but unresolved issue in predictive maintenance, as industrial environments typically involve large amounts of electromagnetic interference and mechanical noise that can severely degrade the signal quality....
Microexpressions are subtle facial movements that occur within an extremely brief time frame, often revealing suppressed emotions. These expressions hold significant importance across various fields, including security monitoring and human–comp...
Agricultural losses due to post-harvest diseases can reach up to 30% of total production. Detecting diseases in fruits at an early stage is crucial to mitigate losses and ensure the quality and health of fruits. However, this task is challenging due...
Recently, transformers have demonstrated notable improvements in natural advanced visual tasks. In the field of computer vision, transformer networks are beginning to supplant conventional convolutional neural networks (CNNs) due to their global rece...
According to proteomics technology, as impacted by the complexity of sampling in the experimental process, several problems remain with the reproducibility of mass spectrometry experiments, and the peptide identification and quantitative results cont...
Pose transfer methods often struggle to simultaneously preserve fine-grained clothing textures and facial details, especially under large pose variations. To address these limitations, we propose a model based on the Multi-scale attention guided pose...
Sun glare poses a significant challenge in Advanced Driver Assistance Systems (ADAS) due to its potential to obscure important visual information, reducing accuracy in detecting road signs, obstacles, and lane markings. Effective sun glare mitigation...
Sensor-based human activity recognition with wearable devices has captured the attention of researchers in the last decade. The possibility of collecting large sets of data from various sensors in different body parts, automatic feature extraction, a...
In mineral processing, visual-based online particle size analysis systems depend on high-precision image segmentation to accurately quantify ore particle size distribution, thereby optimizing crushing and sorting operations. However, due to multi-sca...
Fractures affect nearly 9.45% of the South Korean population, with radiography being the primary diagnostic tool. This research employs a machine-learning methodology that integrates HyperColumn techniques with the convolutional block attention modul...
Cloud segmentation is a fundamental step in accurately acquiring cloud cover. However, due to the nonrigid structures of clouds, traditional cloud segmentation methods perform worse than expected. In this paper, a novel deep convolutional neural netw...
The unsafe action of miners is one of the main causes of mine accidents. Research on underground miner unsafe action recognition based on computer vision enables relatively accurate real-time recognition of unsafe action among underground miners. A d...
Time-domain signal models have been widely applied to single-channel music source separation tasks due to their ability to overcome the limitations of fixed spectral representations and phase information loss. However, the high acoustic similarity an...
Lysine succinylation is an important post-translational modification, whose abnormalities are closely related to the occurrence and development of many diseases. Therefore, exploring effective methods to identify succinylation sites is helpful for di...
An improved maritime object detection algorithm, SRC-YOLO, based on the YOLOv4-tiny, is proposed in the foggy environment to address the issues of false detection, missed detection, and low detection accuracy in complicated situations. To confirm the...
Classification of land use and land cover from remote sensing images has been widely used in natural resources and urban information management. The variability and complex background of land use in high-resolution imagery poses greater challenges fo...
Multi-scale context information, especially long-range dependency, has shown to be beneficial for speaker verification (SV) tasks. In this paper, we propose three methods to systematically explore long-range context SV feature extraction based on Res...
Current deep learning-based change detection approaches mostly produce convincing results by introducing attention mechanisms to traditional convolutional networks. However, given the limitation of the receptive field, convolution-based methods fall...
Synthetic aperture radar (SAR) imagery change detection (CD) is still a crucial and challenging task. Recently, with the boom of deep learning technologies, many deep learning methods have been presented for SAR CD, and they achieve superior performa...
Accurate segmentation of fine roots in field rhizotron imagery is essential for high-throughput root system analysis but remains challenging due to limitations of traditional methods. Traditional methods for root quantification (e.g., soil coring, ma...
With the rapid advancement of deepfake technology, the detection of low-quality synthetic facial images has become increasingly challenging, particularly in cases involving low resolution, blurriness, or noise. Traditional detection methods often exh...
Emotions are classified into the valence dimension (positive and negative) and the arousal dimension (low and high). Using electrocardiogram (ECG) phase space diagrams and a deep learning approach, emotional states were identified in this study. The...
Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, nam...
EEG-based emotion recognition has become an important part of human–computer interaction. To solve the problem that single-modal features are not complete enough, in this paper, we propose a multimodal emotion recognition method based on the at...
Power distribution systems frequently encounter various fault-causing events. Thus, prompt and accurate fault diagnosis is crucial for maintaining system stability and safety. This study presents an innovative residual block-convolutional block atten...
This study enhances Synthetic Aperture Radar (SAR) ship detection by integrating attention mechanisms, Bi-Level Routing Attention (BRA), Swin Transformer, and a Convolutional Block Attention Module (CBAM) into state-of-the-art YOLO architectures (YOL...
The role that underwater image translation plays assists in generating rare images for marine applications. However, such translation tasks are still challenging due to data lacking, insufficient feature extraction ability, and the loss of content de...
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...
In modern industrial systems, diagnosing faults in the rolling bearings of high-speed rotating machinery remains a considerable challenge due to the scarcity of reliable fault samples and the inherent complexity of the diagnostic task. To address the...
Bearings, as commonly used elements in mechanical apparatus, are essential in transmission systems. Fault diagnosis is of significant importance for the normal and safe functioning of mechanical systems. Conventional fault diagnosis methods depend on...
The semantic segmentation of apples from images plays an important role in the automation of the apple industry. However, existing semantic segmentation methods such as FCN and UNet have the disadvantages of a low speed and accuracy for the segmentat...
To address the low efficiency and high subjectivity of manual interpretation in fluorescence in situ hybridization (FISH) tissue and cell images, this study proposes an intelligent FISH image classification model based on an improved ResNet50 archite...
The damage caused by pests to crops results in reduced crop yield and compromised quality. Accurate and timely pest detection plays a crucial role in helping farmers to defend against and control pests. In this paper, a novel crop pest detection mode...
As a representative new energy source, solar energy has the advantages of easy access to resources and low pollution. However, due to the uncertainty of the external environment, photovoltaic (PV) modules that collect solar energy are often covered b...
Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address...
Spammers have created a new kind of electronic mail (e-mail) called image-based spam to bypass text-based spam filters. Unfortunately, these images contain harmful links that can infect the user’s computer system and take a long time to be dele...
Background: Examining chest radiograph images (CXR) is an intricate and time-consuming process, sometimes requiring the identification of many anomalies at the same time. Lung segmentation is key to overcoming this challenge through different deep le...
The vector-transmitted Citrus Greening (CG) disease, also called Huanglongbing, is one of the most destructive diseases of citrus. Since no measures for directly controlling this disease are available at present, current disease management integrates...
There has been increased interest in recognizing the dynamic and flexible changes in shipborne multi-function radar (MFR) working modes. The working modes determine the distribution of pulse descriptor words (PDWs). However, building the mapping rela...
Underwater object detection and classification pose significant challenges due to environmental factors such as water turbidity and variable lighting conditions. This research proposes a novel approach that integrates advanced imaging techniques with...
Traditional fish farming methods suffer from backward production, low efficiency, low yield, and environmental pollution. As a result of thorough research using deep learning technology, the industrial aquaculture model has experienced gradual matura...
Here we proposed a grape disease identification model based on improved YOLOXS (GFCD-YOLOXS) to achieve real-time detection of grape diseases in field conditions. We build a dataset of 11,056 grape disease images in 15 categories, based on 2566 origi...
COVID-19 can cause acute infectious diseases of the respiratory system, and may probably lead to heart damage, which will seriously threaten human health. Electrocardiograms (ECGs) have the advantages of being low cost, non-invasive, and radiation fr...
Pork is the most widely consumed meat product in the world, and achieving accurate detection of individual pigs is of great significance for intelligent pig breeding and health monitoring. Improved pig detection has important implications for improvi...
Agricultural mechanization occupies a key position in modern agriculture. Aiming at the fruit recognition target detection part of the picking robot, a mango recognition method based on an improved YOLOv4 network structure is proposed, which can quic...
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