Network For Recognition Research Articles

Multiscale attention-over-attention network for retinal disease recognition in OCT radiology images

Retinal disease recognition using Optical Coherence Tomography (OCT) images plays a pivotal role in the early diagnosis and treatment of conditions. However, the previous attempts relied on extracting single-scale features often refined by stacked layered attentions. This paper presents a novel deep learning-based Multiscale Feature Enhancement via a Dual Attention Network specifically designed for retinal disease recognition in OCT images. Our approach leverages the EfficientNetB7 backbone to extract multiscale features from OCT images, ensuring a comprehensive representation of global and local retinal structures. To further refine feature extraction, we propose a Pyramidal Attention mechanism that integrates Multi-Head Self-Attention (MHSA) with Dense Atrous Spatial Pyramid Pooling (DASPP), effectively capturing long-range dependencies and contextual information at multiple scales. Additionally, Efficient Channel Attention (ECA) and Spatial Refinement modules are introduced to enhance channel-wise and spatial feature representations, enabling precise localization of retinal abnormalities. A comprehensive ablation study confirms the progressive impact of integrated blocks and attention mechanisms that enhance overall performance. Our findings underscore the potential of advanced attention mechanisms and multiscale processing, highlighting the effectiveness of the network. Extensive experiments on two benchmark datasets demonstrate the superiority of the proposed network over existing state-of-the-art methods.

Advanced Hybrid Neural Networks for Accurate Recognition of the Extended Alphabet and Dynamic Signs in Mexican Sign Language (MSL)

The Mexican deaf community primarily uses Mexican Sign Language (MSL) for communication, but significant barriers arise when interacting with hearing individuals unfamiliar with the language. Learning MSL requires a substantial commitment of at least 18 months, which is often impractical for many hearing people. To address this gap, we present an MSL-to-Spanish translation system that facilitates communication through a spelling-based approach, enabling deaf individuals to convey any idea while simplifying the AI’s task by limiting the number of signs to be recognized. Unlike previous systems that focus exclusively on static signs for individual letters, our solution incorporates dynamic signs, such as “k”, “rr”, and “ll”, to better capture the nuances of MSL and enhance expressiveness. The proposed Hybrid Neural Network-based algorithm integrates these dynamic elements effectively, achieving an F1 score of 90.91%, precision of 91.25%, recall of 91.05%, and accuracy of 91.09% in the extended alphabet classification. These results demonstrate the system’s potential to improve accessibility and inclusivity for the Mexican deaf community.

Recognizing human activities using light-weight and effective machine learning methodologies

Background Human activity recognition (HAR) is increasingly important in enhancing healthcare systems by enabling accurate monitoring of individuals' movements through sensor data. This paper is motivated by the need to improve the accuracy of HAR, particularly for applications in e-health systems, where reliable activity detection can lead to better health outcomes. The study explores six prominent machine learning techniques—decision tree, random forest, linear regression, Naïve Bayes, k-nearest neighbor, and neural networks—to determine which methods can most effectively predict activities like walking, sitting, standing, laying, walking upstairs, and walking downstairs. Methods We employed these six machine learning algorithms to analyze a comprehensive dataset derived from various sensors. Each model was rigorously trained and evaluated to compare its effectiveness in recognizing human activities. The experiments aimed to identify strengths and weaknesses in each approach, with particular emphasis on advanced techniques such as random forest, convolutional neural networks (CNNs), and gated recurrent networks (GRNs). Results The experimental evaluation revealed that the random forest classifier, CNN, GRN, and neural networks delivered promising results, achieving high accuracy levels. Notably, the neural network model excelled, attaining an impressive accuracy of 98%. In contrast, the Naïve Bayes model did not meet the performance expectations set by the other algorithms. Conclusions This research effectively classifies activities such as sitting, standing, laying, walking, walking downstairs, and walking upstairs, underscoring the potential of machine learning in HAR. The findings highlight the superior performance of neural networks in enhancing activity recognition, which could lead to advanced applications in e-health systems and improve overall healthcare monitoring strategies.

Multivariate variational mode decomposition and 1D residual neural network for subtle feature recognition of rolling bearings

Multivariate variational mode decomposition and 1D residual neural network for subtle feature recognition of rolling bearings

Neurons to heartbeats: spiking neural networks for electrocardiogram pattern recognition

Neurons to heartbeats: spiking neural networks for electrocardiogram pattern recognition

YOLOv8-GABNet: An Enhanced Lightweight Network for the High-Precision Recognition of Citrus Diseases and Nutrient Deficiencies

YOLOv8-GABNet: An Enhanced Lightweight Network for the High-Precision Recognition of Citrus Diseases and Nutrient Deficiencies

Automated Recognition of Submerged Body-like Objects in Sonar Images Using Convolutional Neural Networks

The Police Robot for Inspection and Mapping of Underwater Evidence (PRIME) is an uncrewed surface vehicle (USV) currently being developed for underwater search and recovery teams to assist in crime scene investigation. The USV maps underwater scenes using sidescan sonar (SSS). Test exercises use a clothed mannequin lying on the seafloor as a target object to evaluate system performance. A robust, automated method for detecting human body-shaped objects is required to maximise operational functionality. The use of a convolutional neural network (CNN) for automatic target recognition (ATR) is proposed. SSS image data acquired from four different locations during previous missions were used to build a dataset consisting of two classes, i.e., a binary classification problem. The target object class consisted of 166 196 × 196 pixel image snippets of the underwater mannequin, whereas the non-target class consisted of 13,054 examples. Due to the large class imbalance in the dataset, CNN models were trained with six different imbalance ratios. Two different pre-trained models (ResNet-50 and Xception) were compared, and trained via transfer learning. This paper presents results from the CNNs and details the training methods used. Larger datasets are shown to improve CNN performance despite class imbalance, achieving average F1 scores of 97% in image classification. Average F1 scores for target vs background classification with unseen data are only 47% but the end result is enhanced by combining multiple weak classification results in an ensemble average. The combined output, represented as a georeferenced heatmap, accurately indicates the target object location with a high detection confidence and one false positive of low confidence. The CNN approach shows improved object detection performance when compared to the currently used ATR method.

Dynamic spatial-temporal topology graph network for skeleton-based action recognition

Dynamic spatial-temporal topology graph network for skeleton-based action recognition

Integrating path signature and pen-tip trajectory features for online handwriting Yi text recognition

Recognizing online handwriting Yi text is crucial for recording and preserving Yi literature. However, the scarcity of online handwriting Yi text datasets has limited relevant research, impeding the process of Yi informatization. In this work, we use synthetic data to train models, and an Online Handwriting Yi Text Recognition Network (YTRN) is proposed, which extracts robust character features to address the gap between synthetic and real data. YTRN adeptly learns the spatial structure features from path signature feature maps and captures trajectory features from the pen-tip trajectories. Subsequently, an innovative adaptive feature fusion module integrates these two sets of features to yield more comprehensive and robust character representations. Experiments on our newly collected Yi-OLHWDB2.0 dataset demonstrate that our method outperforms previous approaches, achieving an impressive 95.67% accuracy. This highlights the model’s effectiveness in extracting comprehensive and robust features from path signature maps and pen-tip trajectories, significantly enhancing recognition accuracy and generalization.

Automated detection of stale beef from electronic nose data

AbstractAccurate detection of stale beef on the market is important for protecting the legitimate rights and interests of consumers. To this end, we combined electronic nose measurements with machine learning technology to classify beef samples. We used an electronic nose to collect information about the odor characteristics of different beef samples and used linear discriminant analysis to reduce data dimensionality. We then classified samples using the following algorithms: extreme gradient boosting, logistic regression, K‐nearest neighbor, random forest, support vector machine, and neural networks for pattern recognition. We assessed model performance using a 10‐fold cross‐validation technique. All these methods reached an accuracy of 95% or above, with F1 scores and AUC values above 0.96. The support vector machine algorithm outperformed all other models, achieving perfect recognition with 100% accuracy and F1/AUC scores of 1.0. Our study demonstrates that electronic nose data combined with support vector machine can be used to successfully discriminate between stale and fresh beef, paving the way for novel research directions in the detection of stale beef.

Field-free spin–orbit torque magnetization switching in Pt/CoTb devices grown on flexible substrates for neuromorphic computing

Flexible spintronic devices based on spin–orbit torque (SOT)-induced perpendicular magnetization switching (PMS) have attracted increasing attention due to their high storage intensity and good programming capability. However, to achieve deterministic PMS, an in-plane auxiliary magnetic field is required, which greatly limits its application. Here, we show that “robust” magnetic field-free SOT-driven PMS is realized in the oblique sputtered Pt/CoTb multilayers grown on a flexible polyimide substrate. “Robust” means the magnetic field-free SOT switching is highly repeatable and stable after 100 bending cycles under various bending conditions. Additionally, the fabricated flexible multilayers exhibit nearly linear and nonvolatile multistate plasticity as synapses and a nonlinear sigmoid activation function when acting as neurons. We construct a fully connected neural network for handwritten digit recognition, achieving an over 96.27% recognition rate. Our findings may spur further investigations on the SOT-based flexible spintronic devices for wearable artificial intelligence applications.

Detecting command injection attacks in web applications based on novel deep learning methods

Web command injection attacks pose significant security threats to web applications, leading to potential server information leakage or severe server disruption. Traditional detection methods struggle with the increasing complexity and obfuscation of these attacks, resulting in poor identification of malicious code, complicated feature extraction processes, and low detection efficiency. To address these challenges, a novel detection model, the Convolutional Channel-BiLSTM Attention (CCBA) model, is proposed, leveraging deep learning techniques to enhance the identification of web command injection attacks. The model utilizes dual CNN convolutional channels for comprehensive feature extraction and employs a BiLSTM network for bidirectional recognition of temporal features. An attention mechanism is also incorporated to assign weights to critical features, improving the model’s detection performance. Experimental results demonstrate that the CCBA model achieves 99.3% accuracy and 98.2% recall on a real-world dataset. To validate the robustness and generalization of the model, tests were conducted on two widely recognized public cybersecurity datasets, consistently achieving over 98% accuracy. Compared to existing methods, the proposed model offers a more effective solution for identifying web command injection attacks.

Occluded facial expression recognition based on deep learning

Abstract. When employing Convolutional Neural Networks (CNNs) for facial expression recognition, several challenges are often encountered, such as facial occlusions, the limited size of reliable expression datasets, and inadequate precision in recognition outcomes. This paper preprocesses the dataset to enhance its reliability. By leveraging data synthesis and augmentation techniques, it employs a method of randomly generating occlusion blocks to integrate and expand the dataset. Based on the ResNet-18 network, the model is optimized by incorporating an attention mechanism, thereby improving the network's precision and robustness in recognizing facial expressions.

RJAN: Region‐based joint attention network for 3D shape recognition

AbstractAs an essential field of multimedia and computer vision, 3D shape recognition has attracted much research attention in recent years. Multiview‐based approaches have demonstrated their superiority in generating effective 3D shape representations. Typical methods usually extract the multiview global features and aggregate them together to generate 3D shape descriptors. However, there exist two disadvantages: First, the mainstream methods ignore the comprehensive exploration of local information in each view. Second, many approaches roughly aggregate multiview features by adding or concatenating them together. The information loss for some discriminative characteristics limits the representation effectiveness. To address these problems, a novel architecture named region‐based joint attention network (RJAN) was proposed. Specifically, the authors first design a hierarchical local information exploration module for view descriptor extraction. The region‐to‐region and channel‐to‐channel relationships from different granularities can be comprehensively explored and utilised to provide more discriminative characteristics for view feature learning. Subsequently, a novel relation‐aware view aggregation module is designed to aggregate the multiview features for shape descriptor generation, considering the view‐to‐view relationships. Extensive experiments were conducted on three public databases: ModelNet40, ModelNet10, and ShapeNetCore55. RJAN achieves state‐of‐the‐art performance in the tasks of 3D shape classification and 3D shape retrieval, which demonstrates the effectiveness of RJAN. The code has been released on https://github.com/slurrpp/RJAN.

DMFGAN: a multifeature data augmentation method for grape leaf disease identification.

The use of deep learning techniques to identify grape leaf diseases relies on large, high-quality datasets. However, a large number of images occupy more computing resources and are prone to pattern collapse during training. In this paper, a depth-separable multifeature generative adversarial network (DMFGAN) was proposed to enhance grape leaf disease data. First, a multifeature extraction block (MFEB) based on the four-channel feature fusion strategy is designed to improve the quality of the generated image and avoid the problem of poor feature learning ability of the adversarial generation network caused by the single-channel feature extraction method. Second, a depth-based D-discriminator is designed to improve the discriminator capability and reduce the number of model parameters. Third, SeLU activation function was substituted for DCGAN activation function to overcome the problem that DCGAN activation function was not enough to fit grape leaf disease image data. Finally, an MFLoss function with a gradient penalty term is proposed to reduce the mode collapse during the training of generative adversarial networks. By comparing the visual indicators and evaluation indicators of the images generated by different models, and using the recognition network to verify the enhanced grape disease data, the results show that the method is effective in enhancing grape leaf disease data. Under the same experimental conditions, DMFGAN generates higher quality and more diverse images with fewer parameters than other generative adversarial networks. The mode breakdown times of generative adversarial networks in training process are reduced, which is more effective in practical application.

An Improved Multi-Scale Feature Extraction Network for Rice Disease and Pest Recognition

In the process of rice production, rice pests are one of the main factors that cause rice yield reduction. To implement prevention and control measures, it is necessary to accurately identify the types of rice pests and diseases. However, the application of image recognition technologies focused on the agricultural field, especially in the field of rice disease and pest identification, is relatively limited. Existing research on rice diseases and pests has problems such as single data types, low data volume, and low recognition accuracy. Therefore, we constructed the rice pest and disease dataset (RPDD), which was expanded through data enhancement methods. Then, based on the ResNet structure and the convolutional attention mechanism module, we proposed a Lightweight Multi-scale Feature Extraction Network (LMN) to extract multi-scale features at a finer granularity. The proposed LMN model achieved an average classification accuracy of 95.38% and an F1-Score of 94.5% on the RPDD. The parameter size of the model is 1.4 M, and the FLOPs is 1.65 G. The results suggest that the LMN model performs rice disease and pest classification tasks more effectively than the baseline ResNet model by significantly reducing the model size and improving accuracy.

A GCN and Transformer complementary network for skeleton-based action recognition

Graph Convolution Networks (GCNs) have been widely used in skeleton-based action recognition. Although there are significant progress, the inherent limitation still lies in the restricted receptive field of GCN, hindering its ability to extract global dependencies effectively. And the joints that are structurally separated can also have strong correlation. Previous works rarely explore local and global correlations of joints, leading to insufficiently model the complex dynamics of skeleton sequences. To address this issue, we propose a GCN and Transformer complementary network (GTC-Net) that allows parallel communications between GCN and Transformer domains. Specifically, we introduce a graph convolution and self-attention combined module (GAM), which can effectively leverage the complementarity of GCN and self-attention to perceive local and global dependencies of joints for the human body. Furthermore, in order to address the problems of long-term sequence ordering and position detection, we design a position-aware module (PAM), which can explicitly capture the ordering information and unique identity information for body joints of skeleton sequence. Extensive experiments on NTU RGB+D 60 and NTU RGB+D 120 datasets are conducted to evaluate our proposed method. The results demonstrate that our method can achieve competitive results on both datasets.

YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO

Pedestrian detection is a critical task in computer vision; however, mainstream algorithms often struggle to achieve high detection accuracy in complex scenarios, particularly due to target occlusion and the presence of small objects. This paper introduces a novel pedestrian detection algorithm, YOLO-ESL, based on the YOLOv7 framework. YOLO-ESL integrates the ELAN-SA module, designed to enhance feature extraction, with the LGA module, which improves feature fusion. The ELAN-SA module optimizes the flexibility and efficiency of small object feature extraction, while the LGA module effectively integrates multi-scale features through local and global attention mechanisms. Additionally, the CIOUNMS algorithm addresses the issue of target loss in cases of high overlap, improving boundary box filtering. Evaluated on the VOC2012 pedestrian dataset, YOLO-ESL achieved an accuracy of 93.7%, surpassing the baseline model by 3.0%. Compared to existing methods, this model not only demonstrates strong performance in handling occluded and small object detection but also remarkable robustness and efficiency.

ASK-HAR: Attention-Based Multi-Core Selective Kernel Convolution Network for Human Activity Recognition

Human Activity Recognition (HAR) is an increasingly popular field of study aimed at automatically identifying and categorizing human movements and activities using various tracking devices and measurements, such as sensors and cameras. Smartphones have emerged as a prevalent sensor modality for HAR, offering abundant data on an individual’s movements through GPS, accelerometers, and gyroscopes. In conventional convolutional neural networks (CNNs), the artificial neurons within each feature layer typically possess identical receptive fields (RFs). This paper introduces a novel model, ASK-HAR (Attention-based Multi-Core Selective Kernel Convolution Network for HAR), which enhances HAR by performing kernel selection between multiple branches with different RFs using attention mechanisms. The selective kernel mechanism is leveraged to optimize HAR performance. Additionally, the CBAM attention module is employed for time series feature extraction and activity recognition within the overall framework. Extensive experiments conducted on benchmark HAR datasets, including UCI-HAR, USC-HAD, WISDM, PAMAP2, and DSADS, demonstrate that the ASK-HAR model consistently achieves high accuracy across all datasets.

3D skeleton aware driver behavior recognition framework for autonomous driving system

The recognition of the driver’s behaviors inside an autonomous vehicle can effectively address emergency handling in autonomous driving and is crucial for ensuring the driver’s safety. Driver behavior recognition is a challenging task due to factors such as variations, diversities, complexities, and strong interferences in behaviors. In this paper, to realize the application in the autonomous driving scenes, a novel 3D skeleton aware behavior recognition framework is proposed to recognize various driver behaviors in autonomous driving systems. First, a 3D human pose estimation network (Pose-GTFNet) with temporal Transformer and spatial graph convolutional network (GCN) is designed to infer 3D human poses from 2D pose sequences. Second, based on the obtained 3D human pose sequences, a behavior recognition network (Beh-MSFNet) with multi-skeleton feature fusion is designed to recognize driver behaviors. In the experiments, the Pose-GTFNet and Beh-MSFNet can get the best performance compared with most state-of-the-art (SOTA) methods on the Human3.6M human pose dataset, JHMDB and SHREC action recognition dataset, respectively. In addition, the proposed driver behavior recognition framework can achieve SOTA performance on the Drive&Act and Driver-Skeleton driver behavior datasets.