Automatic Recognition Method Research Articles

Window Attention Convolution Network (WACN): A Local Self-Attention Automatic Modulation Recognition Method

Window Attention Convolution Network (WACN): A Local Self-Attention Automatic Modulation Recognition Method

Advancements in Learning-Based Techniques for Automated Floor Plan Analysis on Raster Images: A Comparative Study

Automated floor plan analysis and recognition have long been focal points in computer science research. Recently, there has been a notable increase in the use of learning-based techniques to automatically reorganize floor plans from raster images. This advancement aims to extract valuable insights from architectural drawings, which are essential for understanding building layouts and their intended functions. These drawings often feature a variety of notations and constraints, and the lack of standardized notation leads to significant variability in both style and semantics across different floor plans. Addressing this challenge is a key focus of this review. This paper provides an extensive literature survey to tackle the issue of variability in floor plans. The review concentrates on methodologies that treat floor plans as raster images, with particular attention to learning-based approaches. By offering concise summaries of datasets, research scopes, and specific tasks, this review aims to guide future research and development in the fields of construction and design. The in-depth examination of automatic floor plan analysis and recognition methods presented here contributes to the evolving field of computer-assisted architectural understanding and design.

Improved Speaker Recognition System Using Automatic Lip Recognition

The paper is focused on the relevant problem of speech recognition using additional sources besides the voice itself, in conditions in which the quality or availability of audio information is inadequate (for example, in the presence of noise or additional speakers). This is achieved by using automatic lip recognition (ARL) methods, which rely on non-acoustic biosignals generated by the human body during speech production. Among the applications of this approach are medical applications, as well as processing voice commands in languages with poor audio conditions. The aim of this work is to create a system for speech recognition based on a combination of speaker lip recognition (SSI) and context prediction. To achieve this goal, the following tasks were performed: to substantiate the systems for recognizing voice commands of a silent voice interface (SSI) based on a combination of two neural network architectures, to implement a model for recognizing visemes based on the CNN neural network architecture and an encoder-decoder architecture for the LSTM neural recurrent network model for analyzing and predicting the context of a speaker’s speech. The developed system was tested on a chosen dataset. The results show that the recognition error in different conditions averages from 4,34% to 5,12% for CER and from 5,52% to 6,06% for WER for the proposed ALR system in 7 experiments, which is an advantage over the LipNet project, which additionally processes audio data for the original without noise.

GCNet: Ground Collapse Prediction Based on the Ground-Penetrating Radar and Deep Learning Technique

Manual methods to detect and identify potential ground collapse hazards have a high volume of workload and rely on manual skills and experience strongly, with substantial inconsistencies. This paper proposes an automatic recognition method of potential underground hazards and designs an object detection-based algorithm to predict the hidden danger of ground collapse, called GCNet. The GCNet is a ground collapse hazard detection network, which is developed to detect underground hazards, including cavities, poor soil quality, pipelines, and soil background. The GCNet uses the deep residual network ResNet101 and feature pyramid network (FPN) to extract features and a task coordination network (TCN) to identify the category and location of hidden underground hazards. Further, a feature enhancement method based on the regional binary pattern is proposed to improve the accuracy of the proposed model by expanding the ground-penetrating radar (GPR) data by adding multi-level features to GPR images. The experiments are conducted on a large amount of real GPR data and experiment results show that the proposed automatic recognition method can surpass the existing deep learning-based methods in hidden underground hazard recognition and identification.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems. However, the electromagnetic environment of modern battlefields is complex, and the signal-to-noise ratio (SNR) of such environments is usually low, which makes it difficult to implement accurate recognition of radio fuzes. To solve the above problem, a radio fuze automatic modulation recognition (AMR) method for low-SNR environments is proposed. First, an adaptive denoising algorithm based on data rearrangement and the two-dimensional (2D) fast Fourier transform (FFT) (DR2D) is used to reduce the noise of the intercepted radio fuze intermediate frequency (IF) signal. Then, the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices (GLCMs), and support vector machines (SVMs) are used for classification. The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of −10 dB and above, which is higher than that of other typical algorithms. The trained SVM classification model achieves an average recognition accuracy of more than 96% on seven modulation types and recognition accuracies of more than 94% on each modulation type under SNRs of -12 dB and above, which represents a good AMR performance of radio fuzes under low SNRs.

Does Technology Affect Nutrition Education Perspectives?

The importance of various prevention efforts, including implementing nutrition education especially among children and adolescents, is growing due to noncommunicable diseases like hypertension, obesity, and diabetes that are common among children and adults. Most studies conducted in the previous few decades have noted that using technology in learning environments—including computer-based software, web-based learning, and hardware—creates a remarkable learning and teaching environment. For health professionals like dietitians who provide nutrition education, the ease of data recording, easy transmission and re-accessibility increases the use of various technologies. These technologies present spesific challenges, such as rising costs, user information needs, and age-specific unsuitability. Wearable and artificial intelligence technologies allow individuals to be monitored instantly. With distance education technologies, the opportunity to reach experts regardless of time and place makes it easier to get the correct information. The use of photography and automatic recognition methods in determining the amount of food supports reaching accurate food consumption records. With systems where individuals can see and follow their own data, it becomes easier for individuals to monitor behavior changes. It is stated that, nutritional applications could be more effective when guidance is established based on the attitudes, students’ achievement, and experience. This review focused on the impacts of mobile and web-based nutrition education.

MGFGNet: an automatic underwater acoustic target recognition method based on the multi-gradient flow global feature enhancement network

The recognition of underwater acoustic targets plays a crucial role in marine vessel monitoring. However, traditional underwater target recognition models suffer from limitations, including low recognition accuracy and slow prediction speed. To address these challenges, this article introduces a novel approach called the Multi-Gradient Flow Global Feature Enhancement Network (MGFGNet) for automatic recognition of underwater acoustic targets. Firstly, a new spectrogram feature fusion scheme is presented, effectively capturing both the physical and brain-inspired features of the acoustic signal. This fusion technique enhances the representation of underwater acoustic data, resulting in more accurate recognition results. Moreover, MGFGNet utilizes the multi-gradient flow network and incorporates a multi-dimensional feature enhancement technique to achieve fast and precise end-to-end recognition. Finally, a loss function is introduced to mitigate the influence of unbalanced data sets on model recognition performance using Taylor series. This further enhances model recognition performance. Experimental evaluations were conducted on the DeepShip dataset to assess the performance of our proposed method. The results demonstrate the superiority of MGFGNet, achieving a recognition rate of 99.1%, which significantly surpasses conventional methods. Furthermore, MGFGNet exhibits improved efficiency compared to the widely used ResNet18 model, reducing the parameter count by 51.28% and enhancing prediction speed by 33.9%. Additionally, we evaluated the generalization capability of our model using the ShipsEar dataset, where MGFGNet achieves a recognition rate of 99.5%, indicating its superior performance when applied to unbalanced data. The promising results obtained in this study highlight the potential of MGFGNet in practical applications.

Automatic Recognition Method of Low SNR Regions Based on U-net

The SNR (signal-to-noise ratio, SNR) attribute of seismic data is often used to evaluate the processing effect and help finding special geological bodies. Most of the SNR extraction methods need manual analysis to determine the location and time window, which has low precision and long consumption. In addition, the existing methods cannot accurately identify the boundary of low SNR region. This paper presents an automatic recognition method of low SNR regions of seismic data, which uses U-net (U-shaped fully convolutional network) to detect the boundaries of low SNR regions. The core of the method is to convert the recognition of SNR distribution into an image segmentation problem. We gives the specific process, takes the theoretical simulation data with different SNR as samples for network training, and uses the well trained model to segment seismic data with different SNR. The practical application shows the method has strong generalization ability, significantly improves the accuracy and efficiency of SNR recognition, and greatly reduces the workload of manual analysis.

Solar Radio Burst Detection Based on the MobileViT-SSDLite Lightweight Model

Real-time detection of solar radio bursts is crucial in solar physics research and space weather forecasting. However, current research on the automatic detection of solar radio bursts is limited to identifying the presence or absence of solar radio bursts or recognizing only a single type of burst, such as type II or III. Furthermore, existing methods cannot learn spectral and temporal features and often suffer from the drawbacks of large network models, resulting in slow speeds. This paper proposes an automatic recognition and localization method based on a lightweight object detection model for solar radio burst events. We collected observation data from e-CALLISTO and established a data set containing type II, III, IV, and V solar radio bursts. To address the real-time requirements of practical applications and consider the temporal and frequency domain information of spectrogram images, we improved a vision transformer with a self-attention mechanism and adopted a lightweight model for detection. The experimental results demonstrate that our proposed method achieves an average precision at a 50% intersection-over-union threshold of 78.2% and a recall rate of 92% on the established solar radio burst data set. Additionally, the model operates at a detection speed of 54.8 frames s–1, where a frame refers to a spectral image with a duration of 15 minutes, enabling efficient automated detection and localization of type II, III, IV, and V solar radio bursts.

Automatic Recognition of Blood Cell Images with Dense Distributions Based on a Faster Region-Based Convolutional Neural Network

In modern clinical medicine, the important information of red blood cells, such as shape and number, is applied to detect blood diseases. However, the automatic recognition problem of single cells and adherent cells always exists in a densely distributed medical scene, which is difficult to solve for both the traditional detection algorithms with lower recognition rates and the conventional networks with weaker feature extraction capabilities. In this paper, an automatic recognition method of adherent blood cells with dense distribution is proposed. Based on the Faster R-CNN, the balanced feature pyramid structure, deformable convolution network, and efficient pyramid split attention mechanism are adopted to automatically recognize the blood cells under the conditions of dense distribution, extrusion deformation, adhesion and overlap. In addition, the Align algorithm for region of interest also contributes to improving the accuracy of recognition results. The experimental results show that the mean average precision of cell detection is 0.895, which is 24.5% higher than that of the original network model. Compared with the one-stage mainstream networks, the presented network has a stronger feature extraction capability. The proposed method is suitable for identifying single cells and adherent cells with dense distribution in the actual medical scene.

MBC-Net: long-range enhanced feature fusion for classifying remote sensing images

PurposeClassification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.Design/methodology/approachThis work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.FindingsExtensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.Originality/valueMBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

New Approach for an Affective Computing- Driven Quality of Experience (QoE) Prediction

In human interactions, emotion recognition is crucial. For this reason, the topic of computer-vision approaches for automatic emotion recognition is currently being extensively researched. Processing multi-channel electroencephalogram (EEG) information is one of the most researched methods for automatic emotion recognition. This paper presents a new model for an affective computing-driven Quality of Experience (QoE) prediction. In order to validate the proposed model, a publicly available dataset is used. The dataset contains EEG, ECG, and respiratory data and is focused on a multimedia QoE assessment context. The EEG data are retained on which the differential entropy and the power spectral density are calculated with an observation window of three seconds. These two features were extracted to train several deep-learning models to investigate the possibility of predicting QoE with five different factors. The performance of these models is compared, and the best model is optimized to improve the results. The best results were obtained with an LSTM-based model, presenting an F1-score from 68% to 78%. An analysis of the model and its features shows that the Delta frequency band is the least necessary, that two electrodes have a higher importance, and that two other electrodes have a very low impact on the model's performances.

Toward enriched decoding of mandarin spontaneous speech

A deep neural network (DNN)-based automatic speech recognition (ASR) method for enriched decoding of Mandarin spontaneous speech is proposed. It adopts an enhanced approach over the baseline model built with factored time delay neural networks (TDNN-f) and rescored with RNNLM to first building a baseline system composed of a TDNN-f acoustic model (AM), a trigram language model (LM), and a recurrent neural network language model (RNNLM) to generate a word lattice. It then sequentially incorporates a multi-task Part-of-Speech-RNNLM (POS-RNNLM), a hierarchical prosodic model (HPM), and a reduplication-word LM (RLM) into the decoding process by expanding the word lattice and performing rescoring to improve recognition performance and enrich the decoding output with syntactic parameters of POS and punctuation (PM), prosodic tags of word-juncture break types and syllable prosodic states, and an edited recognition text with reduplication words being eliminated. Experimental results on the Mandarin conversational dialogue corpus (MCDC) showed that SER, CER, and WER of 13.2 %, 13.9 %, and 19.1 % were achieved when incorporating the POS-RNNLM and HPM into the baseline system. They represented relative SER, CER, and WER reductions of 7.7 %, 7.9 % and 5.0 % as comparing with those of the baseline system. Futhermore, the use of the RLM resulted in additional 3 %, 4.6 %, and 4.5 % relative SER, CER, and WER reductions through eliminating reduplication words.

Impulsive Aggression Break, Based on Early Recognition Using Spatiotemporal Features

The study of human behaviors aims to gain a deeper perception of stimuli that control decision making. To describe, explain, predict, and control behavior, human behavior can be classified as either non-aggressive or anomalous behavior. Anomalous behavior is any unusual activity; impulsive aggressive, or violent behaviors are the most harmful. The detection of such behaviors at the initial spark is critical for guiding public safety decisions and a key to its security. This paper proposes an automatic aggressive-event recognition method based on effective feature representation and analysis. The proposed approach depends on a spatiotemporal discriminative feature that combines histograms of oriented gradients and dense optical flow features. In addition, the principal component analysis (PCA) and linear discriminant analysis (LDA) techniques are used for complexity reduction. The performance of the proposed approach is analyzed on three datasets: Hockey-Fight (HF), Stony Brook University (SBU)-Kinect, and Movie-Fight (MF), with accuracy rates of 96.5%, 97.8%, and 99.6%, respectively. Also, this paper assesses and contrasts the feature engineering and learned features for impulsive aggressive event recognition. Experiments show promising results of the proposed method compared to the state of the art. The implementation of the proposed work is available here.

Automatic Face Recognition of Target Images Based on Deep Learning Algorithms

The current research results of face recognition have poor recognition accuracy, time-consuming and high information loss rate. A method of automatic face recognition based on depth learning algorithm is proposed. The three primary RGB images are transformed into color saturated HSV images, and the dynamic range is extended by changing the non-linearity according to the conversion results. Based on the extended situation, the membership function is used to map the target image to the blurred plane, and the contrast enhancement of the image is completed. The enhanced image is substituted into the image segmentation, and the image eigenvalues are extracted preliminarily. The seed of image segmentation is generated by the eigenvalues. The growing seed regions are merged by the color distance and the texture distance to achieve the target image segmentation. Based on image segmentation, LBP operator is used to extract the local texture features of face twice, and then a deep convolution network model is constructed. The shared weights of convolution network model and pooling down-sampling technique are used to reduce the complexity of the model. The top layer of the model forms a feature classification surface of face image, fuses the constraint conditions, and obtains the trained face. The deep convolution network model is used to extract features from face images and complete face recognition. The experimental results show that the method has good accuracy, high efficiency and low information loss rate.

A high-precision automatic recognition method based on target detection for nanometer scaled precipitates or carbides in different alloys

The recognition and statistical analysis of nanometer scaled second phase (NSP), such as precipitates or carbides in alloys is considered to be very important for optimization of mechanical properties. Statistical analysis of the size, number, and distribution of NSPs is required, and manual statistics are difficult and inefficient. In order to solve the above problems, this paper provides an object detection technology for the automatic identification of the NSPs in different alloys. A dataset is constructed by labeling the NSPs in typical transmission electron micrographs (TEM) photographs using a labelImg software. First of all, based on the YOLOv3 object detection network in deep learning, changing the network input size is compatible with the TEM image size. Secondly, using two improved schemes, a priori box K-means clustering and CBAM attention mechanism, a variety of class NSPs detection network is proposed. The statistical results show that the network developed herein has strong applicability. This network can be used for identification for the NSP in several types of alloys, i.e., Cu–Sc alloy, 4Cr5MoSiV1 and Cu–Ni–Si alloy. The accuracy of this network for the recognition of these NSP in Cu–Sc alloy (Cu4Sc precipitates), 4Cr5MoSiV1 (carbides) and Cu–Ni–Si alloy (Ni2Si precipitates) is 93.55%, 81.82% and 93.13%, respectively.

New Method for Automatic Recognition of Mexican Indigenous Languages: Comparative Performance of Classifiers

New Method for Automatic Recognition of Mexican Indigenous Languages: Comparative Performance of Classifiers

Damage identification of railway bridge underwater foundations based on optical images

Diagnosing the health status of the underwater foundation of railway bridges is critical for railway environmental assessment. However, traditional underwater optical image detection technology suffers from image blurring. Thus, this paper develops an underwater foundation damage identification method for railway bridges based on optical images, which exploits the various underwater propagation characteristics of different light source wavelengths. This paper mainly reflects the differential characteristics of underwater basic damage images under different wavelength responses by constructing inter-spectral variance feature functions, local feature functions of images, and global feature functions of images. An optical image enhancement model that integrates different wavelength response features has been proposed to effectively enhance basic underwater optical images. An underwater basic damage area automatic recognition method has been developed that can accurately and efficiently extract the boundary contour of the damaged area. Based on combining field test data, the feasibility and effectiveness of this method were verified through comparative analysis with other methods. The results indicate that the damage identification ability of the method proposed in this paper is improved compared to traditional methods, which can effectively improve the quality of underwater optical images and improve the recognition accuracy of damaged areas. It is a new method of underwater damage identification based on optical image, which can provide more effective technical support for the health diagnosis of underwater railway bridges.

Automatic Detection and Recognition Method of Chinese Clay Tiles Based on YOLOv4: A Case Study in Macau

ABSTRACT With the rapid growth of global urbanization and the rising demand for sustainable development, it is essential to study the performance and durability of building materials. As a traditional and widely employed building material, Chinese clay tiles play a significant role in traditional Chinese architecture. However, traditional methods for assessing structural surface damage necessitate the time-consuming and labor-intensive assessment and judgment of trained professionals. Consequently, it is crucial to employ machine learning techniques for automatic damage type identification. This study identifies the types of damage to Chinese clay tiles in Macau by employing machine learning techniques and the YOLOv4 object detection model. A total of 363 photographs of on-site Chinese clay tiles were used as training samples, and 200 epochs of the model training were performed. The primary findings of this study are as follows: (1) The machine learning method, based on the YOLOv4 model, provides an effective and precise solution for the automatic identification of Chinese clay tiles damage types, overcoming the human and time-cost constraints of conventional evaluation methods. (2) The detection accuracy of the detection model in this study is 95.42% for the detection of Chinese clay tiles cracks, 80.91% for the detection of stains, and 89.34% for the detection of surface wear, with an overall accuracy of 88.98%, which meets the basic detection requirements. (3) The experimental results demonstrate the viability and efficacy of the proposed method for identifying clay tile damage types and provide a method reference for the preservation and sustainable development of historical buildings.

A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test.

Chest computed tomography (CT) is a critical tool in the diagnosis of pulmonary cryptococcosis as approximately 30% of normal immunity individuals may not exhibit any significant symptoms or laboratory findings. Pulmonary cryptococcosis granuloma and lung adenocarcinoma can appear similar on noncontrast chest CT. This study evaluates the use of an integrated model that was developed based on radiomic features combined with demographic and radiological features to differentiate pulmonary cryptococcosis nodules from lung adenocarcinomas. Preoperative chest CT images for 215 patients with solid pulmonary nodules with histopathologically confirmed lung adenocarcinoma and cryptococcosis infection were collected from two clinical centers (108 cases in the training set and 107 cases in the test set divided by the different hospitals). Radiomics models were constructed based on nodular lesion volume (LV), 5-mm extended lesion volume (ELV), and perilesion volume (PLV). A demoradiological model was constructed using logistic regression based on demographic information (age, sex) and 12 radiological features (location, number, shape and specific imaging signs). Both models were used to build an integrated model, the performance of which was assessed using the test set. A junior and a senior radiologist evaluated the nodules. Receiver operating characteristic (ROC) curve analysis was conducted, and areas under the curve (AUCs), sensitivity (SEN), and specificity (SPE) of the models were calculated and compared. Among the radiomics models, AUCs of the LV, ELV, and PLV were 0.558, 0.757, and 0.470, respectively. Age, lesion number, and lobular sign were identified as independent discriminative features providing an AUC of 0.77 in the demoradiological model (SEN 0.815, SPE 0.642). The integrated model achieved the highest AUC of 0.801 (SEN 0.759, SPE 0.755), which was significantly higher than that obtained by a junior radiologist (AUC =0.689, P=0.024) but showed no significant difference from that of the senior radiologist (AUC =0.784, P=0.388). An integrated model with radiomics and demoradiological features improves discrimination of cryptococcosis granulomas from solid adenocarcinomas on noncontrast CT. This model may be an effective strategy for machine complementation to discrimination by radiologists, and whole-lung automated recognition methods might dominate in the future.