Enhance Recognition Accuracy Research Articles

Photodual-Responsive Anthracene-Based 2D Covalent Organic Framework for Optoelectronic Synaptic Devices.

To facilitate the development of efficient neuromorphic perception and computation, it is crucial to explore optoelectronic synaptic devices that integrate perceptual and computational capabilities. Various materials such as oxide semiconductors, conjugated organic polymers, transition metal sulfides, perovskite materials, and metal nanoparticles, along with their composites, are utilized in constructing these devices. However, optoelectronic synaptic devices based on 2D covalent organic frameworks (COFs) is rarely reported. In this study, an anthracene-based 2D COF (COF-DaTp) film is prepared using a room-temperature interface-confined strategy and utilized it as the active layer in an optoelectronic synaptic device with an Al/COF-DaTp/ITO configuration. The device demonstrated dual optoelectronic modulation, exhibiting significant optoelectronic resistive switching in response to light pulses, achieving 32 photoconductive states. Moreover, it exhibited history-dependent memristive behavior in voltage scans and electrical pulses, with a comparable diversity of 32 conductive states. The photodual-responsive properties of the COF-DaTp-based synaptic device enable it to simultaneously perform optical sensing and basic image denoising and recognition tasks, significantly enhancing recognition accuracy and reducing the number of training epochs compared to datasets without noise mitigation. This work opens the door for the application of 2D COF-based optoelectronic synaptic devices in visual computational processing.

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.

Radar signal modulation identification using global context vision transformer

Abstract The accurate identification of phase-coded radar waveforms is critical in electronic warfare (EW) systems, particularly with the increasing use of low probability of intercept (LPI) radars. However, current methods struggle to reliably recognize these waveforms at low signal-to-noise ratios (SNRs). To address this challenge, we propose an AI-based Global Context Vision Transformer (GC-ViT) model that leverages short-time Fourier transform (STFT) phase spectrum for feature extraction. The GC-ViT model enhances recognition accuracy by incorporating both local and global self-attention mechanisms, enabling more effective identification of phase-coded signals in noisy environments. Experimental results demonstrate that the proposed method achieves approximately 80\% recognition accuracy at an SNR of -12 dB, which significantly outperforms existing techniques. This advancement in radar waveform recognition enhances the situational awareness and decision-making capability of EW systems in complex electromagnetic environments.

Deep Learning Method of Precious Wood Image Classification Based on Microscopic Computed Tomography

Correctly identifying precious wood species is crucial for import and export trade and furniture material identification. This study utilizes nondestructive testing (Microscopic Computed Tomography, Micro-CT) to capture microscopic images of the transverse, radial, and tangential sections of 24 precious wood species, creating a comprehensive dataset. The SLConNet deep learning model is developed, enhancing recognition accuracy through multi-scale convolution and an improved residual block structure. The experiment results show that the classification accuracy of the transverse, radial and tangential sections is 98.72, 96.75 and 95.36 % respectively when the gain value is 0.8. The model outperforms traditional models like Alexnet, ResNet50, Inception-V3, and Xception. This research highlights the efficiency of nondestructive testing in obtaining a large number of microscopic wood images, compared to traditional anatomical methods. The SLConNet model showcases high accuracy in precision, recall, and specificity, suggesting its potential for widespread applications in wood classification.

Improving the industrial defect recognition in radiographic testing by pre-training on medical radiographs

Deep learning methodologies have gained substantial traction for defect recognition in industrial radiographic testing including welds, castings and other fields. Regardless of the deep learning utilized, it has emerged as a standard configuration to use a model pre-trained from ImageNet to accelerate convergence and enhance recognition accuracy. However, there is a significant gap between the domain of natural images and industrial radiographs, raising the question of whether there might be a superior pre-training method than relying on ImageNet pre-training. Fortunately, medical radiographs are more similar to industrial radiographs than natural images because of the same imaging method. In this paper, we initially utilize numerous medical radiographic images from CheXpert dataset to train a pre-trained CNN model. Then, we apply this model to four distinct tasks within two radiographic testing scenarios to validate its advantages and generalization capabilities. Finally, experiments on multiple datasets indicate that our method brings more benefits than ImageNet pre-training or training from scratch, with a F1 score improvement of 3.41 %–13.72 % for defect classification and a mIoU improvement of 1.05 %–6.58 % for defect segmentation. It demonstrates that pre-training from medical radiographs provides a cost-free improvement for all kinds of tasks in industrial defect recognition.

Cross-Modal Harmony: Low-Rank Fusion for Enhanced Artificial Emotion Recognition

Emotion recognition has become a subject of considerable interest in recent times, owing to its diverse and far-reaching applications in various fields. These applications span from enhancing human-computer interactions to assessing mental health and improving entertainment systems. The proposed study presents a novel approach for emotion recognition by fusing audio and video modalities using low-rank fusion techniques. The proposed methodology leverages the complementary nature of audio and video data in capturing emotional cues. Audio data often encapsulates tone, speech patterns, and vocal nuances, while video data captures facial expressions, body language, and gestures. However, the challenge lies in effectively integrating these two modalities to enhance recognition accuracy. To address the challenge, it employs low-rank fusion, a dimensionality reduction technique that extracts the most informative features from both modalities while minimizing redundancy. Furthermore, it presents the implementation of the chosen low-rank fusion algorithm in a real-world emotion recognition system. The results can contribute to advancing the field of emotion recognition by providing a practical and efficient solution for combining audio and video data to achieve more robust and accurate emotion classification. Keywords: Deep Learning; Emotion Recognition; Human-Computer Interaction; Low-Rank Fusion; Multimodal Fusion.

A Novel YOLOv10-DECA Model for Real-Time Detection of Concrete Cracks

The You Only Look Once (YOLO) series algorithms have been widely adopted in concrete crack detection, with attention mechanisms frequently being incorporated to enhance recognition accuracy and efficiency. However, existing research is confronted by two primary challenges: the suboptimal performance of attention mechanism modules and the lack of explanation regarding how these mechanisms influence the model’s decision-making process to improve accuracy. To address these issues, a novel Dynamic Efficient Channel Attention (DECA) module is proposed in this study, which is designed to enhance the performance of the YOLOv10 model in concrete crack detection, and the effectiveness of this module is visually demonstrated through the application of interpretable analysis algorithms. In this paper, a concrete dataset with a complex background is used. Experimental results indicate that the DECA module significantly improves the model’s accuracy in crack localization and the detection of discontinuous cracks, outperforming the existing Efficient Channel Attention (ECA). When compared to the similarly sized YOLOv10n model, the proposed YOLOv10-DECA model demonstrates improvements of 4.40%, 3.06%, 4.48%, and 5.56% in precision, recall, mAP50, and mAP50-95 metrics, respectively. Moreover, even when compared with the larger YOLOv10s model, these performance indicators are increased by 2.00%, 0.04%, 2.27%, and 1.12%, respectively. In terms of speed evaluation, owing to the lightweight design of the DECA module, the YOLOv10-DECA model achieves an inference speed of 78 frames per second, which is 2.5 times faster than YOLOv10s, thereby fully meeting the requirements for real-time detection. These results demonstrate that an optimized balance between accuracy and speed in concrete crack detection tasks has been achieved by the YOLOv10-DECA model. Consequently, this study provides valuable insights for future research and applications in this field.

PIoT‐oriented multi‐target recognition of substation infrared images driven by deep learning

AbstractSubstation infrared imaging plays a crucial role in condition monitoring and fault detection of Power Internet of Things (PIoT). However, the accurate and efficient recognition of multiple targets in substation infrared images remains a challenging task. This paper proposes a deep learning‐based multi‐target recognition framework for substation infrared images in PIoT. This paper presents a method for recognizing various electrical equipment in infrared images of substations using a faster region‐based convolutional neural network (Faster RCNN). The optimization of Faster RCNN includes class rectification inspired by non‐maximum suppression (NMS), enabling the correction of misclassified equipment parts and enhancing recognition accuracy. The approach combines NMS and class rectification to retain region proposals with optimal recognition performance. Experimental results demonstrate the effectiveness of the proposed method in improving the recognition accuracy of electrical equipment in infrared images.

Annotated Dataset for Training Cloud Segmentation Neural Networks Using High-Resolution Satellite Remote Sensing Imagery

The integration of satellite data with deep learning has revolutionized various tasks in remote sensing, including classification, object detection, and semantic segmentation. Cloud segmentation in high-resolution satellite imagery is a critical application within this domain, yet progress in developing advanced algorithms for this task has been hindered by the scarcity of specialized datasets and annotation tools. This study addresses this challenge by introducing CloudLabel, a semi-automatic annotation technique leveraging region growing and morphological algorithms including flood fill, connected components, and guided filter. CloudLabel v1.0 streamlines the annotation process for high-resolution satellite images, thereby addressing the limitations of existing annotation platforms which are not specifically adapted to cloud segmentation, and enabling the efficient creation of high-quality cloud segmentation datasets. Notably, we have curated the Annotated Dataset for Training Cloud Segmentation (ADTCS) comprising 32,065 images (512 × 512) for cloud segmentation based on CloudLabel. The ADTCS dataset facilitates algorithmic advancement in cloud segmentation, characterized by uniform cloud coverage distribution and high image entropy (mainly 5–7). These features enable deep learning models to capture comprehensive cloud characteristics, enhancing recognition accuracy and reducing ground object misclassification. This contribution significantly advances remote sensing applications and cloud segmentation algorithms.

Research on traffic sign detection model with improved YOLOv8s

Abstract With the rapid advancement of intelligent driving technologies such as autonomous driving and driver assistance, the significance of traffic sign recognition in road and traffic safety has become increasingly prominent. Addressing the challenges posed by the current mainstream traffic sign detection models, which include complex background structures and the detection of small targets, this study proposes an enhanced network for detecting traffic signs based on YOLOv8s. Firstly, we modify the convolutional component of the backbone by replacing the initial convolutions with the Alterable Kernel Convolution (AKConv) network, thereby enhancing feature extraction efficiency and network performance. Secondly, we introduce the Global Attention Mechanism (GAM) within the network to effectively aggregate features, improve reconstruction performance, and maintain low computational and storage costs. Lastly, we integrate the Bi-directional Feature Pyramid Network (BiFPN) into the YOLOv8s framework, enabling the model to capture multi-scale features efficiently and enhance recognition accuracy. Experimental results conducted on the CCTSDB-2021 show the efficacy of the study improved YOLOv8s model. Precision, recall, and mAP@50% achieve impressive values of 96.0%, 79.4%, and 88.3%, respectively. These metrics represent a 4.6% improvement in precision, a 0.4% enhancement in the recall, and a 2% enhancement in mAP@50%, compared with the original YOLOv8s model. Moreover, this improved model exhibits greater precision when compared with other existing models.

Editorial: Bridging the Gap: AI and Sign Language Recognition– A Path Toward Inclusive Communication

The integration of Artificial Intelligence (AI) and sign language recognition is a hot topic in the field of AI+Science, aimed at addressing communication barriers faced by the deaf and hard-of-hearing communities. This paper examines the integration of AI with sign language recognition, highlighting its potential to bridge communication gaps for the deaf and hard-of-hearing. It reviews the evolution of sign language recognition from data gloves to computer vision and underscores the role of extensive databases. The paper also discusses the benefits of multi-modal AI models in enhancing recognition accuracy. It highlights the importance of government and industry support, ethical data practices, and user-centered design in advancing this technology. The challenges and opportunities of integrating this technology into daily life, including technical, interface, and ethical considerations, are explored, emphasizing the need for user-focused solutions and innovative technical approaches.

Partial Discharge Detection Technique Based on Full Convolutional Network with Channel-wise Convolution and Channel Mixing

Abstract The occurrence of partial discharge (PD) serves as an indication of power equipment failure, which, if not given due attention, may result in severe power accidents. Traditionally, the detection of PD involves applying various pre-processing techniques to the original signal and extracting specialized signal features that are subsequently classified using a classifier. The process is intricate and demands extensive expertise. One drawback of the conventional detection approach lies in its failure to consider the information conveyed between the phases of a three-phase electrical signal during the process of identifying PD in medium voltage three-phase cables. The present study proposes a novel method for detecting PDs, which is based on channel-wise convolution and channel mixing(C2CM) using full convolutional networks. Firstly, a set of pulses for each phase of a three-phase signal is extracted and combined to form a three-phase pulse set, serving as the input to the network. Subsequently, we introduce a FCN-C2CM network designed specifically for PD detection, capable of learning both the time-domain features of the input signal and the phase features of the three-phase signal, thereby enhancing recognition accuracy. We evaluated our approach using online testing tools on publicly available datasets, with experimental results demonstrating its superiority over existing.

Intention inference for space targets using deep convolutional neural network

Intention inference for space targets is crucial for space situational awareness. This paper introduces a rapid and precise method for recognizing the intentions of non-cooperative space targets using a deep convolutional neural network (CNN). By employing a relative orbital dynamics model, an analysis of relative motion was performed, resulting in the identification of 11 distinct motion intentions for space targets. This study also describes how to generate relative motion trajectory images to create a training set for intention inference, effectively converting the problem into one of image recognition and classification. Extensive simulations were carried out to fine-tune the network hyperparameters, and the results highlight the exceptional performance of the proposed CNN-based method, which achieved an accuracy of 99.682%. This method significantly enhances recognition accuracy over other neural network-based methods for space objects and offers considerable potential for applications like spacecraft collision avoidance and strategic maneuvers among space targets.

A complex-valued convolutional fusion-type multi-stream spatiotemporal network for automatic modulation classification

Automatic Modulation Classification (AMC) is crucial in non-cooperative communication systems as it facilitates the identification of interference signals with minimal prior knowledge. Although there have been significant advancements in Deep Learning (DL) within the field of AMC, leveraging the inherent relationships between In-phase (I) and Quadrature-phase (Q) components, and enhance recognition accuracy under low signal-to-noise ratio (SNR) conditions remains a challenge. This study introduces a complex-valued convolutional fusion-type multi-stream spatiotemporal network (CC-MSNet) for AMC, which combines spatial and temporal feature extraction modules for modulation recognition. Experimental results demonstrate that the CC-MSNet performs well on three benchmark datasets, RML2016.10a, RML2016.10b, and RML2016.04c, with average recognition accuracy of 62.86%, 65.08%, and 71.12%. It also performs excellently in low SNR environments below 0dB, significantly outperforming other networks.

Rotation-invariant image recognition using interconnected floating-gate phototransistor

Rotational invariance is fundamental for robust image recognition systems, ensuring accurate analysis irrespective of image orientation. However, existing systems predominantly reliant on software often encounter challenges such as increased computational demands and compromises between processing speed and accuracy. In this study, we propose leveraging the interconnected floating-gate (FG) structure as an effective hardware-level solution to achieve rotational invariance in image recognition. Our design features a reconfigurable two-dimensional material FG phototransistor array, where each processing unit integrates four sensory devices sharing a common FG. This configuration facilitates uniform distribution of stored charges across the interconnected FG layer, which is typically made of metal, enabling consistent application of a single weight matrix to images across varied rotational conditions. The photoactive material, tungsten diselenide (WSe2), possesses a distinctive bipolar property that facilitates both hole and electron tunneling into the FG layer. This property directly contributes to the efficiency of state transition within the setup and improves its overall adaptability. In this manner, our design achieves stable and predictable outputs in recognizing identical digital numbers regardless of their rotation, while also demonstrating variable performance essential for accurately distinguishing between different digital numbers. This dual capability guarantees both the adaptability and precision required for rotation-invariant image recognition, suggesting that our work may open up a promising venue for exploring advanced hardware designs, such as optimized interconnected FG architectures, tailored for enhancing recognition accuracy and efficiency in the field of intelligent visual systems.

Enhancing recognition accuracy and efficiency through intelligent frame selection in uncontrolled conditions

Enhancing recognition accuracy and efficiency through intelligent frame selection in uncontrolled conditions

YOLOv8-Coal: a coal-rock image recognition method based on improved YOLOv8.

To address issues such as misdetection and omission due to low light, image defocus, and worker occlusion in coal-rock image recognition, a new method called YOLOv8-Coal, based on YOLOv8, is introduced to enhance recognition accuracy and processing speed. The Deformable Convolution Network version 3 enhances object feature extraction by adjusting sampling positions with offsets and aligning them closely with the object's shape. The Polarized Self-Attention module in the feature fusion network emphasizes crucial features and suppresses unnecessary information to minimize irrelevant factors. Additionally, the lightweight C2fGhost module combines the strengths of GhostNet and the C2f module, further decreasing model parameters and computational load. The empirical findings indicate that YOLOv8-Coal has achieved substantial enhancements in all metrics on the coal rock image dataset. More precisely, the values for AP50, AP50:95, and AR50:95 were improved to 77.7%, 62.8%, and 75.0% respectively. In addition, optimal localization recall precision (oLRP) were decreased to 45.6%. In addition, the model parameters were decreased to 2.59M and the FLOPs were reduced to 6.9G. Finally, the size of the model weight file is a mere 5.2 MB. The enhanced algorithm's advantage is further demonstrated when compared to other commonly used algorithms.

Optimizing Flipped Classroom Teaching in College Basketball Through 'Internet +' Algorithm

The integration of the flipped classroom model with network technology has revolutionized college basketball education, shaping a new era of instructional dynamics. This study delves into the optimization process of algorithms within the “Internet +” technology framework, with a focus on enhancing recognition accuracy. Through a comprehensive exploration of the application of the “Internet +” algorithm across different academic grades in the context of flipped classroom teaching for college basketball, insights from both students and teachers are synthesized. Empirical tests underscore the effectiveness of incorporating “Internet +” technology into the pedagogical framework of college basketball flipped classrooms. By identifying and addressing prevalent teaching challenges inherent in this setting, the research proposes strategic interventions aimed at elevating the overall teaching quality of college basketball flipped classrooms.

GaitAE: A Cognitive Model-Based Autoencoding Technique for Gait Recognition

Gait recognition is a long-distance biometric technique with significant potential for applications in crime prevention, forensic identification, and criminal investigations. Existing gait recognition methods typically introduce specific feature refinement modules on designated models, leading to increased parameter volume and computational complexity while lacking flexibility. In response to this challenge, we propose a novel framework called GaitAE. GaitAE efficiently learns gait representations from large datasets and reconstructs gait sequences through an autoencoder mechanism, thereby enhancing recognition accuracy and robustness. In addition, we introduce a horizontal occlusion restriction (HOR) strategy, which introduces horizontal blocks to the original input sequences at random positions during training to minimize the impact of confounding factors on recognition performance. The experimental results demonstrate that our method achieves high accuracy and is effective when applied to existing gait recognition techniques.

Research on Tool Wear Monitoring Based on Enhanced Convolutional Neural Networks

Abstract Identifying the wear status of cutting tools during the machining process is essential because failure to promptly replace severely worn tools can significantly impact the quality of workpiece machining. Presently, machine learning methods are predominantly utilized for monitoring cutting tool wear status. However, these methods rely on manual feature extraction and exhibit low accuracy. This study introduces a novel RRP-Net model, built upon the RepVgg and ResNet frameworks, integrating a parameter-free self-attention mechanism called SimAM to expedite the model’s solving speed without increasing parameters. Within the foundational module of the model, a structural reparameterization approach is employed to transform the multi-branch structure during training into a single-branch structure during validation. This method not only enhances model accuracy but also accelerates the model validation process. The publicly available cutting data from PHM2010 is employed for model training and validation. The findings demonstrate that RRP-Net surpasses classical convolutional neural network models in identifying cutting tool wear status within the PHM2010 dataset, achieving an average accuracy of 98.65% and enhancing recognition accuracy on relevant datasets by 2.41%. To verify the model’s practical applicability, specificity and recall during the Break stage are computed at 99.73% and 98.18%, respectively, affirming the model’s exceptional robustness and stability. The heightened accuracy and efficiency of RRP-Net further broaden its applicability within the industrial domain.