A system of number recognition with a graphic user interface （GUI） is implemented on the embedded development platform by using the fuzzy pattern recognition method. An application interface （API） of uC/ OS-Ⅱ is ...A system of number recognition with a graphic user interface （GUI） is implemented on the embedded development platform by using the fuzzy pattern recognition method. An application interface （API） of uC/ OS-Ⅱ is used to implement the features of multi-task concurrency and the communications among tasks. Handwriting function is implemented by the improvement of the interface provided by the platform. Fuzzy pattern recognition technology based on fuzzy theory is used to analyze the input of handwriting. A primary system for testing is implemented. It can receive and analyze user inputs from both keyboard and touch-screen. The experimental results show that the embedded fuzzy recognition system which uses the technology which integrates two ways of fuzzy recognition can retain a high recognition rate and reduce hardware requirements.展开更多
Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech recog-nition.In military applications,deep neural networks can detect equipment and recognize objects.In military e...Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech recog-nition.In military applications,deep neural networks can detect equipment and recognize objects.In military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural networks.There have been no previous studies on the detection of real rifle numbers using real rifle image datasets.In this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient.The proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer learningmethods.In the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 algorithmmodel.The detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical datasets.We used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental environment.TensorFlow was used as the machine learning library.Experimental results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle numbers.The proposed method is effective in detecting rifle numbers.展开更多
Football is one of the most-watched sports,but analyzing players’per-formance is currently difficult and labor intensive.Performance analysis is done manually,which means that someone must watch video recordings and ...Football is one of the most-watched sports,but analyzing players’per-formance is currently difficult and labor intensive.Performance analysis is done manually,which means that someone must watch video recordings and then log each player’s performance.This includes the number of passes and shots taken by each player,the location of the action,and whether or not the play had a successful outcome.Due to the time-consuming nature of manual analyses,interest in automatic analysis tools is high despite the many interdependent phases involved,such as pitch segmentation,player and ball detection,assigning players to their teams,identifying individual players,activity recognition,etc.This paper proposes a system for developing an automatic video analysis tool for sports.The proposed system is the first to integrate multiple phases,such as segmenting the field,detecting the players and the ball,assigning players to their teams,and iden-tifying players’jersey numbers.In team assignment,this research employed unsu-pervised learning based on convolutional autoencoders(CAEs)to learn discriminative latent representations and minimize the latent embedding distance between the players on the same team while simultaneously maximizing the dis-tance between those on opposing teams.This paper also created a highly accurate approach for the real-time detection of the ball.Furthermore,it also addressed the lack of jersey number datasets by creating a new dataset with more than 6,500 images for numbers ranging from 0 to 99.Since achieving a high perfor-mance in deep learning requires a large training set,and the collected dataset was not enough,this research utilized transfer learning(TL)to first pretrain the jersey number detection model on another large dataset and then fine-tune it on the target dataset to increase the accuracy.To test the proposed system,this paper presents a comprehensive evaluation of its individual stages as well as of the sys-tem as a whole.展开更多
Traditional calibration method for the digital inclinometer relies on manual inspection,and results in its disadvantages of complicated process,low-efficiency and human errors easy to be introduced.To improve both the...Traditional calibration method for the digital inclinometer relies on manual inspection,and results in its disadvantages of complicated process,low-efficiency and human errors easy to be introduced.To improve both the calibration accuracy and efficiency of digital inclinometer,an automatic digital inclinometer calibration system was developed in this study,and a new display tube recognition algorithm was proposed.First,a high-precision automatic turntable was taken as the reference to calculate the indication error of the inclinometer.Then,the automatic inclinometer calibration control process and the digital inclinometer zero-setting function were formulated.For display tube recognition,a new display tube recognition algorithm combining threading method and feature extraction method was proposed.Finally,the calibration system was calibrated by photoelectric autocollimator and regular polygon mirror,and the calibration system error and repeatability were calculated via a series of experiments.The experimental results showed that the indication error of the proposed calibration system was less than 4",and the repeatability was 3.9".A digital inclinometer with the resolution of 0.1°was taken as a testing example,within the calibration points'range of[-90°,90°],the repeatability of the testing was 0.085°,and the whole testing process was less than 90 s.The digital inclinometer indication error is mainly introduced by the digital inclinometer resolution according to the uncertainty evaluation.展开更多
This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network(CNN) using micro Doppler features. Firstly, the time-...This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network(CNN) using micro Doppler features. Firstly, the time-frequency spectrograms are acquired from the radar echo by the short-time Fourier transform.Secondly, based on the obtained spectrograms, a seven-layer CNN architecture is built to recognize the blade-number parity and classify the manoeuvre intention of the rotor target. The constructed architecture contains a leaky rectified linear unit and a dropout layer to accelerate the convergence of the architecture and avoid over-fitting. Finally, the spectrograms of the datasets are divided into three different ratios, i.e., 20%, 33% and 50%,and the cross validation is used to verify the effectiveness of the constructed CNN architecture. Simulation results show that, on the one hand, as the ratio of training data increases, the recognition accuracy of parity and manoeuvre intention is improved at the same signal-to-noise ratio(SNR);on the other hand, the proposed algorithm also has a strong robustness: the accuracy can still reach 90.72% with an SNR of – 6 dB.展开更多
Individual livestock identification is of great importance to precision livestock farming.Liquid nitrogen freezing labeled horse brand is an effective way for livestock individual identification.Along with various tec...Individual livestock identification is of great importance to precision livestock farming.Liquid nitrogen freezing labeled horse brand is an effective way for livestock individual identification.Along with various technological developments,deep-learning-based methods have been applied in such individual marking recognition.In this research,a deep learning method for oriented horse brand location and recognition was proposed.Firstly,Rotational YOLOv5(R-YOLOv5)was adopted to locate the oriented horse brand,then the cropped images of the brand area were trained by YOLOv5 for number recognition.In the first step,unlike classical detection methods,R-YOLOv5 introduced the orientation into the YOLO framework by integrating Circle Smooth Label(CSL).Besides,Coordinate Attention(CA)was added to raise the attention to positional information in the network.These improvements enhanced the accuracy of detecting oriented brands.In the second step,number recognition was considered as a target detection task because of the requirement of accurate recognition.Finally,the whole brand number was obtained according to the sequences of each detection box position.The experiment results showed that R-YOLOv5 outperformed other rotating target detection algorithms,and the AP(Average Accuracy)was 95.6%,the FLOPs were 17.4 G,the detection speed was 14.3 fps.As for the results of number recognition,the mAP(mean Average Accuracy)was 95.77%,the weight size was 13.71 MB,and the detection speed was 68.6 fps.The two-step method can accurately identify brand numbers with complex backgrounds.It also provides a stable and lightweight method for livestock individual identification.展开更多
基金Pre-Research Project of the National Natural Science Foundation of China supported by Southeast University ( NoXJ0605227)
文摘A system of number recognition with a graphic user interface （GUI） is implemented on the embedded development platform by using the fuzzy pattern recognition method. An application interface （API） of uC/ OS-Ⅱ is used to implement the features of multi-task concurrency and the communications among tasks. Handwriting function is implemented by the improvement of the interface provided by the platform. Fuzzy pattern recognition technology based on fuzzy theory is used to analyze the input of handwriting. A primary system for testing is implemented. It can receive and analyze user inputs from both keyboard and touch-screen. The experimental results show that the embedded fuzzy recognition system which uses the technology which integrates two ways of fuzzy recognition can retain a high recognition rate and reduce hardware requirements.
基金supported by the Future Strategy and Technology Research Institute(RN:23-AI-04)of Korea Military Academythe Hwarang-Dae Research Institute(RN:2023B1015)of Korea Military Academy,and Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1I1A1A01040308).
文摘Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech recog-nition.In military applications,deep neural networks can detect equipment and recognize objects.In military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural networks.There have been no previous studies on the detection of real rifle numbers using real rifle image datasets.In this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient.The proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer learningmethods.In the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 algorithmmodel.The detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical datasets.We used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental environment.TensorFlow was used as the machine learning library.Experimental results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle numbers.The proposed method is effective in detecting rifle numbers.
文摘Football is one of the most-watched sports,but analyzing players’per-formance is currently difficult and labor intensive.Performance analysis is done manually,which means that someone must watch video recordings and then log each player’s performance.This includes the number of passes and shots taken by each player,the location of the action,and whether or not the play had a successful outcome.Due to the time-consuming nature of manual analyses,interest in automatic analysis tools is high despite the many interdependent phases involved,such as pitch segmentation,player and ball detection,assigning players to their teams,identifying individual players,activity recognition,etc.This paper proposes a system for developing an automatic video analysis tool for sports.The proposed system is the first to integrate multiple phases,such as segmenting the field,detecting the players and the ball,assigning players to their teams,and iden-tifying players’jersey numbers.In team assignment,this research employed unsu-pervised learning based on convolutional autoencoders(CAEs)to learn discriminative latent representations and minimize the latent embedding distance between the players on the same team while simultaneously maximizing the dis-tance between those on opposing teams.This paper also created a highly accurate approach for the real-time detection of the ball.Furthermore,it also addressed the lack of jersey number datasets by creating a new dataset with more than 6,500 images for numbers ranging from 0 to 99.Since achieving a high perfor-mance in deep learning requires a large training set,and the collected dataset was not enough,this research utilized transfer learning(TL)to first pretrain the jersey number detection model on another large dataset and then fine-tune it on the target dataset to increase the accuracy.To test the proposed system,this paper presents a comprehensive evaluation of its individual stages as well as of the sys-tem as a whole.
基金the National Natural Science Foundation of China(No.61927822)。
文摘Traditional calibration method for the digital inclinometer relies on manual inspection,and results in its disadvantages of complicated process,low-efficiency and human errors easy to be introduced.To improve both the calibration accuracy and efficiency of digital inclinometer,an automatic digital inclinometer calibration system was developed in this study,and a new display tube recognition algorithm was proposed.First,a high-precision automatic turntable was taken as the reference to calculate the indication error of the inclinometer.Then,the automatic inclinometer calibration control process and the digital inclinometer zero-setting function were formulated.For display tube recognition,a new display tube recognition algorithm combining threading method and feature extraction method was proposed.Finally,the calibration system was calibrated by photoelectric autocollimator and regular polygon mirror,and the calibration system error and repeatability were calculated via a series of experiments.The experimental results showed that the indication error of the proposed calibration system was less than 4",and the repeatability was 3.9".A digital inclinometer with the resolution of 0.1°was taken as a testing example,within the calibration points'range of[-90°,90°],the repeatability of the testing was 0.085°,and the whole testing process was less than 90 s.The digital inclinometer indication error is mainly introduced by the digital inclinometer resolution according to the uncertainty evaluation.
基金supported by the National Natural Science Foundation of China (61901514)the Young Talent Program of Air Force Early Warning Academy (TJRC425311G11)。
文摘This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network(CNN) using micro Doppler features. Firstly, the time-frequency spectrograms are acquired from the radar echo by the short-time Fourier transform.Secondly, based on the obtained spectrograms, a seven-layer CNN architecture is built to recognize the blade-number parity and classify the manoeuvre intention of the rotor target. The constructed architecture contains a leaky rectified linear unit and a dropout layer to accelerate the convergence of the architecture and avoid over-fitting. Finally, the spectrograms of the datasets are divided into three different ratios, i.e., 20%, 33% and 50%,and the cross validation is used to verify the effectiveness of the constructed CNN architecture. Simulation results show that, on the one hand, as the ratio of training data increases, the recognition accuracy of parity and manoeuvre intention is improved at the same signal-to-noise ratio(SNR);on the other hand, the proposed algorithm also has a strong robustness: the accuracy can still reach 90.72% with an SNR of – 6 dB.
基金supported by the National Key Research and Development Program of China (Grant No.2023YFD1301801)National Na-ural Science Foundation of China (No.32272931).
文摘Individual livestock identification is of great importance to precision livestock farming.Liquid nitrogen freezing labeled horse brand is an effective way for livestock individual identification.Along with various technological developments,deep-learning-based methods have been applied in such individual marking recognition.In this research,a deep learning method for oriented horse brand location and recognition was proposed.Firstly,Rotational YOLOv5(R-YOLOv5)was adopted to locate the oriented horse brand,then the cropped images of the brand area were trained by YOLOv5 for number recognition.In the first step,unlike classical detection methods,R-YOLOv5 introduced the orientation into the YOLO framework by integrating Circle Smooth Label(CSL).Besides,Coordinate Attention(CA)was added to raise the attention to positional information in the network.These improvements enhanced the accuracy of detecting oriented brands.In the second step,number recognition was considered as a target detection task because of the requirement of accurate recognition.Finally,the whole brand number was obtained according to the sequences of each detection box position.The experiment results showed that R-YOLOv5 outperformed other rotating target detection algorithms,and the AP(Average Accuracy)was 95.6%,the FLOPs were 17.4 G,the detection speed was 14.3 fps.As for the results of number recognition,the mAP(mean Average Accuracy)was 95.77%,the weight size was 13.71 MB,and the detection speed was 68.6 fps.The two-step method can accurately identify brand numbers with complex backgrounds.It also provides a stable and lightweight method for livestock individual identification.
