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Application of Depth learning algorithm in Automatic Processing and Analysis of Sports Images

Computer Systems Science & Engineering 2023年第10期47卷 317-332页

作者： Kai Yang North China University of Water Resources and Electric Power Henan450000China

With the rapid development of sports,the number of sports images has increased *** and automatic processing and analysis of moving images are significant,which can not only facilitate users to quickly search and access moving images but also facilitate staff to store and manage moving image data and contribute to the intellectual development of the sports *** this paper,a method of table tennis identification and positioning based on a convolutional neural network is proposed,which solves the problem that the identification and positioning method based on color features and contour features is not adaptable in various *** the same time,the learning methods and techniques of table tennis detection,positioning,and trajectory prediction are studied.A deep learning framework for recognition learning of rotating flying table tennis is put *** mechanism and methods of positioning,trajectory prediction,and intelligent automatic processing of moving images are studied,and the self-built data sets are trained and verified.

关键词： deep learning algorithm convolutional neural network moving image trajectory intelligent processing

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Classification and Regression of Pinhole Corrosions on Pipelines Based on Magnetic Flux Leakage Signals Using Convolutional Neural Networks

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algorithmS 2024年第8期17卷 347页

作者： Shen, Yufei Zhou, Wenxing Univ Western Ontario Dept Civil & Environm Engn London ON N6A 5B9 Canada

Pinhole corrosions on oil and gas pipelines are difficult to detect and size and, therefore, pose a significant challenge to the pipeline integrity management practice. This study develops two convolutional neural network (CNN) models to identify pinholes and predict the sizes and location of the pinhole corrosions according to the magnetic flux leakage signals generated using the magneto-static finite element analysis. Extensive three-dimensional parametric finite element analysis cases are generated to train and validate the two CNN models. Additionally, comprehensive algorithm analysis evaluates the model performance, providing insights into the practical application of CNN models in pipeline integrity management. The proposed classification CNN model is shown to be highly accurate in classifying pinholes and pinhole-in-general corrosion defects. The proposed regression CNN model is shown to be highly accurate in predicting the location of the pinhole and obtain a reasonably high accuracy in estimating the depth and diameter of the pinhole, even in the presence of measurement noises. This study indicates the effectiveness of employing deep learning algorithms to enhance the integrity management practice of corroded pipelines.

关键词： deep learning algorithm convolutional neural network pipeline corrosion pinhole finite element analysis magnetic flux leakage signal

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Classification of Geometric Forms in Mosaics Using deep Neural Network

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JOURNAL OF IMAGING 2021年第8期7卷 149-149页

作者： Ghosh, Mridul Obaidullah, Sk Md Gherardini, Francesco Zdimalova, Maria Shyampur Siddheswari Mahavidyalaya Dept Comp Sci Howrah 711312 India Aliah Univ Dept Comp Sci & Engn Kolkata 700160 India Univ Modena & Reggio Emilia Dept Engn Enzo Ferrari I-41121 Modena Italy Slovak Univ Technol Bratislava Dept Math & Descript Geometry Bratislava 81005 Slovakia

The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.

关键词： deep learning algorithm convolutional neural networks pattern classification image-based reconstruction cultural heritage

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Research on feature point matching algorithm improvement using depth prediction

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JOURNAL OF ENGINEERING-JOE 2019年第23期2019卷 8905-8909页

作者： Chen, Yongbin Wang, Guitang Wu, Liming Guangdong Univ Technol Guangzhou 510006 Peoples R China

Feature point matching plays an important role in feature-based image registration such as the scale-invariant feature transform algorithm. Feature-based image registration is widely used in visual simultaneous localisation and mapping, augmented reality, self-driving etc. The most meaningful study on feature matching is to improve the accuracy and efficiency and this study pays attention to improving the accuracy by removing the mismatching feature points. Since most of the existed feature-based image registration algorithms are not so strong and efficient enough in mismatch removing, in this study, the authors propose a novel mismatch removal algorithm by incorporating depth prediction into feature matching to improve the performance. In this approach, the depth maps are predicted in pixel-wise through the given red-green-blue images using a deep learning algorithm. Experimental results show that their method outperforms conventional ones in mismatch removing.

关键词： learning (artificial intelligence) image matching feature extraction transforms image registration image colour analysis red-green-blue images feature-based image registration algorithms visual simultaneous localisation feature point matching algorithm improvement deep learning algorithm depth prediction novel mismatch removal algorithm mismatch removing mismatching feature points feature matching

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Feature Extraction for Track Section Status Classification Based on UGW Signals

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SENSORS 2018年第4期18卷 1225-1225页

作者： Yuan, Lei Yang, Yuan Hernandez, Alvaro Shi, Lin Xian Univ Technol Elect Dept Xian 710048 Shaanxi Peoples R China Univ Alcala Elect Dept Madrid 28805 Spain

Track status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain track section, often determining whether it is free or occupied by a train. Different technologies have already been involved in the design of monitoring systems, including ultrasonic guided waves (UGW). This work proposes the use of the UGW signals captured by a track monitoring system to extract the features that are relevant for determining the corresponding track section status. For that purpose, three features of UGW signals have been considered: the root mean square value, the energy, and the main frequency components. Experimental results successfully validated how these features can be used to classify the track section status into free, occupied and broken. Furthermore, spatial and temporal dependencies among these features were analysed in order to show how they can improve the final classification performance. Finally, a preliminary high-level classification system based on deep learning networks has been envisaged for future works.

关键词： track status classification ultrasonic guided wave (UGW) feature extraction temporal and spatial dependencies deep learning algorithm

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Optical frequency multiplication using residual network with random forest regression

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HELIYON 2024年第10期10卷 e30958页

作者： Zhang, Qi Han, Xu Fang, Xinyu Liu, Ming Ge, Ke Jiang, Hong Changchun Univ Technol Coll Elect & Elect Engn 2055 Yanan St Changchun 130012 Peoples R China

In this work, we present a method for optical frequency multiplication utilizing a hybrid deep learning approach that integrates the Residual Network (ResNet) with the Random Forest Regression (RFR) algorithm. Three different frequency multiplication modulation schemes are adopted to illustrate the method, which can obtain suitable parameters for these schemes. Based on the parameters predicted by the algorithm, the 8-tupling, 12-tupling, and 16-tupling mm-wave signals are generated by numerical simulation. The simulation results show that for 8-tupling frequency multiplication, an OSSR (optical sideband suppression ratio) is 30.73 dB and an RFSSR (radio frequency spurious suppression ratio) of 80 GHz is 42.29 dB. For 12-tupling frequency multiplication, the OSSR is 30.09 dB, and the RFSSR of the 120 GHz mm wave is 36.21 dB. For generating 16-tupling frequency mm-wave, an OSSR of 29.86 dB and an RFSSR of 34.52 dB are obtained. In addition, the impact of amplitude fluctuation and bias voltage drift on the quality of mm-wave signals is also studied.

关键词： Frequency multiplication modulation deep learning algorithm Residual network Random forest regression

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Study on the effect of different electrode channel combinations of motor imagery EEG signals on classification accuracy

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JOURNAL OF ENGINEERING-JOE 2019年第23期2019卷 8641-8645页

作者： Zhu, Kai Wang, Shuai Zheng, Dezhi Dai, Mengxi Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing Peoples R China Beihang Univ Sch Comp Sci Beijing Peoples R China

In order to improve the performance of motor imagery brain-computer interface (BCI) based on deep learning algorithm, here, the authors propose an electrode channel combination method. Although motor imagery electro-encephalography (EEG) signals which contain different electrode channels on the scalp surface have an effect on the classification performance, the effect of different electrode channel combinations has not been systematically explored. With the two deep learning models the authors constructed, the authors list some different electrode channel combinations to classify the left fist and right fist motor imagery EEG signals. The results show that the more the number of channels in these combinations, the higher the classification accuracy. However, when the number of channels exceeds 11, the classification accuracy increases slowly, and the classification effect is rarely improved. Therefore, the authors obtain an optimal electrode channel combination to use the electrode channels efficiently and to improve the performance of motor imagery BCI based on deep learning algorithms.

关键词： brain-computer interfaces signal classification electroencephalography learning (artificial intelligence) medical signal processing different electrode channel combinations motor imagery EEG classification accuracy motor imagery brain-computer interface deep learning algorithm electrode channel combination method motor imagery electro-encephalography signals different electrode channels fist motor imagery classification effect optimal electrode channel combination motor imagery BCI

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Application of dynamic time warping optimization algorithm in speech recognition of machine translation

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HELIYON 2023年第11期9卷 e21625页

作者： Jiang, Shaohua Chen, Zheng Fujian Univ Technol Sch Humanities Fuzhou 350118 Peoples R China Krirk Univ Bangkok 10220 Thailand Fujian Normal Univ Concord Univ Coll Fuzhou Fujian Peoples R China

Speech recognition is the foundation of human-computer interaction technology and an important aspect of speech signal processing, with broad application prospects. Therefore, it is very necessary to recognize speech. At present, speech recognition has problems such as low recognition rate, slow recognition speed, and severe interference from other factors. This paper studied speech recognition based on dynamic time warping (DTW) algorithm. By introducing speech recognition, the specific steps of speech recognition were understood. Before performing speech recognition, the speech that needs to be recognized needs to be converted into a speech sequence using an acoustic model. Then, the DTW algorithm was used to preprocess speech recognition, mainly by sampling and windowing the speech. After preprocessing, speech feature extraction was carried out. After feature extraction was completed, speech recognition was carried out. Through experiments, it can be found that the recognition rate of speech recognition on the basis of DTW algorithm was very high. In a quiet environment, the recognition rate was above 93.85 %, and the average recognition rate of the 10 selected testers was 95.8 %. In a noisy environment, the recognition rate was above 91.4 %, and the average recognition rate of the 10 selected testers was 93 %. In addition to high recognition rate, DTW based speech recognition also had a very fast speed for vocabulary recognition. Based on the DTW algorithm, speech recognition not only has a high recognition rate, but also has a faster recognition speed.

关键词： Speech recognition Dynamic time warping algorithm Feature extraction deep learning algorithm

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Pedestrian tracking and counting based on YOLOv4 and deepSORT in Subway Stations

Pedestrian tracking and counting based on YOLOv4 and DeepSOR...

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第35届中国控制与决策会议

作者： Hui Zhang Jie Xu Xiaowen Zhang State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University

Real walking parameters of pedestrians are the basis of research on pedestrian *** detection and tracking technology provides the possibility of accurate walking *** tackle the problem of lower detection accuracy of small-scale pedestrians,the YOLOv4 algorithm has been improved in two ***,the improved YOLOv4 model acts as the detector of deepSORT algorithm for pedestrian tracking and *** the improvement,the YOLOv4 algorithm performs well in both the public and private *** mAP is 90.61% in the private dataset,raising about 3.42%.Detection speeds reach 28.2 and 18.2 fps for processing images and video,*** on the YOLOv4-deepSORT algorithm,trajectories and walking parameters of pedestrians can be *** show the average speed of pedestrians on stairs in subway stations follows the normal distribution,the mean value is 1.27 m/s,and the standard deviation is 0.33 m/s.

关键词： deep learning algorithm pedestrian detection pedestrian tracking trajectories walking parameters

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Multiplex immunofluorescence and single-cell transcriptomic profiling reveal the spatial cell interaction networks in the non-small cell lung cancer microenvironment

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CLINICAL AND TRANSLATIONAL MEDICINE 2023年第1期13卷 e1155页

作者： Peng, Haoxin Wu, Xiangrong Liu, Shaopeng He, Miao Xie, Chao Zhong, Ran Liu, Jun Tang, Chenshuo Li, Caichen Xiong, Shan Zheng, Hongbo He, Jianxing Lu, Xu Liang, Wenhua Guangzhou Med Univ Affiliated Hosp 1 Dept Thorac Oncol & Surg China State Key Lab Resp Dis Guangzhou Peoples R China Guangzhou Med Univ Natl Clin Res Ctr Resp Dis Affiliated Hosp 1 Guangzhou Peoples R China Guangzhou Med Univ Nanshan Sch Dept Clin Med Guangzhou Peoples R China Guangdong Polytech Normal Univ Dept Comp Sci Guangzhou 510006 Peoples R China Dept Artificial Intelligence Res Pazhou Lab Guangzhou Peoples R China Genecast Biotechnol Co Ltd Med Dept Beijing Peoples R China First Peoples Hosp Zhaoqing Dept Med Oncol Zhaoqing Peoples R China Guangzhou Med Univ Dept Thorac Surg Affiliated Hosp 1 Guangzhou Peoples R China China State Key Lab Resp Dis Guangzhou Peoples R China Natl Clin Res Ctr Resp Dis Guangzhou Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China

BackgroundConventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology. MethodsTissue slices of primary tumours from 553 IA similar to IIIB non-small cell lung cancer (NSCLC) cases were stained by multiplex immunofluorescence (mIF) assay for 10 markers, including CD4, CD38, CD20, FOXP3, CD66b, CD8, CD68, PD-L1, CD133 and CD163, evaluating the amounts of 26 phenotypes of cells in tumour nest and tumour stroma. StarDist depth learning model was utilised to determine the spatial location of cells based on mIF graphs. Single-cell RNA sequencing (scRNA-seq) on four primary NSCLC cases was conducted to investigate the putative cell interaction networks. ResultsSpatial proximity among CD20+ B cells, CD4+ T cells and CD38+ T cells (r(2) = 0.41) was observed, whereas the distribution of regulatory T cells was associated with decreased infiltration levels of CD20+ B cells and CD38+ T cells (r(2) = -0.45). Univariate Cox analyses identified closer proximity between CD8+ T cells predicted longer disease-free survival (DFS). In contrast, closer proximity between CD133+ cancer stem cells (CSCs), longer distances between CD4+ T cells and CD20+ B cells, CD4+ T cells and neutrophils, and CD20+ B cells and neutrophils were correlated with dismal DFS. Data from scRNA-seq further showed that spatially adjacent N1-like neutrophils could boost the proliferation and activation of T and B lymphocytes, whereas spatially neighbouring M2-like macrophages showed negative effects. An immune-related risk score (IRRS) system aggregating robust quantitative and spatial prognosticators showed that high-IRRS patients had significantly worse DFS than low-IRRS ones (HR 2.72, 95% CI 1.87-3.94, p < .001). ConclusionsWe developed a framework to analyse the cell interaction networks in tumour microenvironment, reve

关键词： cell interaction networks deep learning algorithm multiplex immunofluorescence single-cell RNA sequencing tumour microenvironment

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