scholarly journals Intelligent Recognition System Based on Contour Accentuation for Navigation Marks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanke Du ◽  
Shuo Sun ◽  
Shi Qiu ◽  
Shaoxi Li ◽  
Mingyang Pan ◽  
...  
Keyword(s):  
Image Recognition ◽  
Recognition Accuracy ◽  
Multiple Scale ◽  
Recognition System ◽  
Attention Mechanism ◽  
Important Task ◽  
Experimental Results ◽  
Classification Model ◽  
Navigation Systems ◽  
Intelligent Recognition

Sensing navigational environment represented by navigation marks is an important task for unmanned ships and intelligent navigation systems, and the sensing can be performed by recognizing the images from a camera. In order to improve the image recognition accuracy, this paper combined a contour accentuation algorithm into a multiple scale attention mechanism-based classification model for navigation marks. Experimental results show that the method increases the accuracy of navigation mark classification from 95.98% to 96.53%. Based on the classification model, an intelligent navigation mark recognition system was developed for the Changjiang Nanjing Waterway Bureau, in which the model is deployed and updated by the TensorFlow Serving.

Recognition System for Fatigue Facial Expression of Driver

2014 ◽  
Vol 644-650 ◽  
pp. 4174-4177
Author(s):  
Xue Mei Wang ◽  
Jia Jun Zhang
Keyword(s):  
Facial Expression ◽  
Image Recognition ◽  
Detection Method ◽  
Recognition Rate ◽  
Recognition System ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Feature Points ◽  
Driver Fatigue ◽  
Recognition Model

In order to improve the accuracy of recognition system for fatigue facial expression of driver, driver fatigue expression of this paper, the detection method for key feature points in the fatigue facial expression image of driver is applied in the paper to establish a fatigue expression image recognition model based on attention mechanism. Experimental results show that the algorithm can improve the recognition rate of driver's expression image, so as to record fatigue expression image of driver more accurate.

scholarly journals Research on Recognition Method of COVID-19 Images Based on Deep Learning

2020 ◽  
Author(s):  
dongshen ji ◽  
yanzhong zhao ◽  
zhujun zhang ◽  
qianchuan zhao
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Small Sample ◽  
Experimental Results ◽  
Ct Image ◽  
Recognition Method ◽  
Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples,the recognition accuracy is not ideal.In this paper,a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, perform small-sample image enhancement and expansion on the converted picture, such as miscut transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning.Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images,even with only a small number of CT image samples.

scholarly journals Study on Comprehensive Calibration and Image Sieving for Coal-Gangue Separation Parallel Robot

2020 ◽  
Vol 10 (20) ◽  
pp. 7059
Author(s):  
Deyong Shang ◽  
Yuwei Wang ◽  
Zhiyuan Yang ◽  
Junjie Wang ◽  
Yue Liu
Keyword(s):  
Coordinate System ◽  
Image Recognition ◽  
Calculation Method ◽  
Calibration Method ◽  
Parallel Robot ◽  
Recognition System ◽  
Conveyor Belt ◽  
Coal Gangue ◽  
Experimental Results ◽  
Robot Calibration

Online sorting robots based on image recognition are key pieces of equipment for the intelligent washing of coal mines. In this paper, a Delta-type, coal gangue sorting, parallel robot is designed to automatically identify and sort scattered coal and gangue on conveyor belts by configuring the image recognition system. Robot calibration technology can reduce the influence of installation error on system accuracy and provides the basis for the robot to accurately track and grab gangue. Due to the fact that the angle deflection error between the conveyor belt coordinate system and the robot coordinate system is not considered in the traditional conveyor belt calibration method, an improved comprehensive calibration method is put forward in this paper. Firstly, the working principle and image recognition and positioning process of the Delta coal gangue sorting robot are introduced. The scale factor parameter Factorc of the conveyor encoder is adopted to characterize the relationship between the moving distance of the conveyor and the encoder. The conveyor belt calibration experiment is described in detail. The transformation matrix between the camera, the conveyor belt, and the robot are obtained after establishment of the three respective coordinate systems. The experimental results show that the maximum cumulative deviation of traditional calibration method is 13.841 mm and the comprehensive calibration method is 3.839 mm. The main innovation of the comprehensive calibration is such that the accurate position of each coordinate in the robot coordinate system can be determined. This comprehensive calibration method is simple and feasible, and can effectively improve system calibration accuracy and reduce robot installation error on the grasping accuracy. Moreover, a calculation method to eliminate duplicate images is put forward, with the frame rate of the vision system set at seven frames per second to avoid image repetition acquisition and missing images. The experimental results show that this calculation method effectively improves the processing efficiency of the recognition system, thereby meeting the demands of the grab precision of coal gangue separation engineering. The goal revolving around “safety with few people and safety with none” can therefore be achieved in coal gangue sorting using robots.

scholarly journals Embedded QR Code Intelligent Recognition Platform Based on Team Progress Algorithm

2016 ◽  
Vol 12 (02) ◽  
pp. 46
Author(s):  
Yuehong Wu
Keyword(s):  
Genetic Algorithm ◽  
General Condition ◽  
Recognition System ◽  
Experimental Results ◽  
Qr Code ◽  
Graphic Display ◽  
Acquisition Process ◽  
Mixed Programming ◽  
Intelligent Recognition ◽  
The Impact

In general condition, QR code often encounters uneven illumination, complex background, contamination and deformation for the reason of the impact on the image acquisition process to make that it is difficult to identify to them in later period and to compare with the recognition results of OR code in the team progress algorithm and genetic algorithm. After that, QR code recognition platform is established to achieve the powerful image and graphic display processing function and to save Visual C++ coding time by using the team genetic algorithm and mixed programming of Matlab and Visual C++ in the article. The experimental results demonstrate the validity of QR code decoding algorithm proposed in the article and the experimental results also demonstrate the feasibility of the design scheme of the embedded QR code recognition system platform proposed in the article.

scholarly journals Research on Recognition Method of COVID-19 Images Based on Deep Learning

2021 ◽  
Vol 271 ◽  
pp. 01039
Author(s):  
Dongsheng Ji ◽  
Yanzhong Zhao ◽  
Zhujun Zhang ◽  
Qianchuan Zhao
Keyword(s):  
Image Recognition ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Small Sample ◽  
Experimental Results ◽  
Fine Tuning ◽  
Ct Image ◽  
Recognition Method ◽  
Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples, the recognition accuracy is not ideal. In this paper, a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, small-sample image enhancement and extension are performed on the transformed image, such as staggered transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning. Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images, even with only a small number of CT image samples.

scholarly journals Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

2021 ◽  
Author(s):  
Linghui Xu ◽  
Jiansong Chen ◽  
Fei Wang ◽  
Yuting Chen ◽  
Wei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Plantar Pressure ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Gait Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Time Performance ◽  
Pathological Gait

Abstract Background: Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information.Methods: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of seventeen children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe in, toe out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified 10-fold cross-validation with recall, precision, and a time cost as metrics.Results: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% recognition accuracy respectively in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing.Conclusions: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

scholarly journals Adversarial Cross-Domain Action Recognition with Co-Attention

2020 ◽  
Vol 34 (07) ◽  
pp. 11815-11822 ◽  
Author(s):  
Boxiao Pan ◽  
Zhangjie Cao ◽  
Ehsan Adeli ◽  
Juan Carlos Niebles
Keyword(s):  
Supervised Learning ◽  
Action Recognition ◽  
Image Recognition ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Single Domain ◽  
Attention Network ◽  
Cross Domain ◽  
The Cross ◽  
Temporal Misalignment

Action recognition has been a widely studied topic with a heavy focus on supervised learning involving sufficient labeled videos. However, the problem of cross-domain action recognition, where training and testing videos are drawn from different underlying distributions, remains largely under-explored. Previous methods directly employ techniques for cross-domain image recognition, which tend to suffer from the severe temporal misalignment problem. This paper proposes a Temporal Co-attention Network (TCoN), which matches the distributions of temporally aligned action features between source and target domains using a novel cross-domain co-attention mechanism. Experimental results on three cross-domain action recognition datasets demonstrate that TCoN improves both previous single-domain and cross-domain methods significantly under the cross-domain setting.

scholarly journals Multiscale Feature Filtering Network for Image Recognition System in Unmanned Aerial Vehicle

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xianghua Ma ◽  
Zhenkun Yang ◽  
Shining Chen
Keyword(s):  
Object Detection ◽  
Unmanned Aerial Vehicle ◽  
Image Recognition ◽  
Visual Recognition ◽  
Recognition System ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Context Information ◽  
Aerial Vehicle ◽  
Feature Filtering

For unmanned aerial vehicle (UAV), object detection at different scales is an important component for the visual recognition. Recent advances in convolutional neural networks (CNNs) have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. However, most existing multiscale feature representation methods simply employ several attention blocks in the attention mechanism to adaptively recalibrate the feature response, which overlooks the context information at a multiscale level. To solve this problem, a multiscale feature filtering network (MFFNet) is proposed in this paper for image recognition system in the UAV. A novel building block, namely, multiscale feature filtering (MFF) module, is proposed for ResNet-like backbones and it allows feature-selective learning for multiscale context information across multiparallel branches. These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel-wise feature responses by emphasizing channel-wise dependencies. Experimental results on CIFAR100 and Tiny ImageNet datasets reflect that the MFFNet achieves very competitive results in comparison with previous baseline models. Further ablation experiments verify that the MFFNet can achieve consistent performance gains in image classification and object detection tasks.

scholarly journals Design and Realization of Computer Image Intelligent Recognition System

2022 ◽  
Vol 2146 (1) ◽  
pp. 012002
Author(s):  
Guibing Xu
Keyword(s):  
Image Recognition ◽  
Computer Technology ◽  
Computer Image ◽  
Digital Images ◽  
Vision System ◽  
Recognition System ◽  
Basic Knowledge ◽  
Human Society ◽  
Machine Vision System ◽  
Intelligent Recognition

Abstract The application of intelligent image recognition technology in life is more and more extensive, especially in the field of computer and multimedia, the research of machine vision system is becoming more and more mature, and the demand of human society for information processing is constantly increasing. This article first analyzes the basic knowledge of digital images based on computer technology, including basic knowledge of digital images, basic knowledge of image filtering and image recognition algorithms. Secondly, this paper studies the design and implementation of computer image intelligent recognition system.

scholarly journals Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Linghui Xu ◽  
Jiansong Chen ◽  
Fei Wang ◽  
Yuting Chen ◽  
Wei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Plantar Pressure ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Gait Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Time Performance ◽  
Pathological Gait

Abstract Background Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information. Methods In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of 17 children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe-in, toe-out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified tenfold cross-validation with recall, precision, and a time cost as metrics. Results The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject recognition accuracy, respectively, in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing. Conclusions In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

Sign in / Sign up

Export Citation Format

Share Document