Image recognition model based on deep learning for remaining oil recognition from visualization experiment

Wang, Yanwei; Liu, Huiqing; Guo, Mingzhe; Shen, Xudong; Han, Bailu; Zhou, Yuhao

doi:10.1016/j.fuel.2021.120216

Cited by 58 publications

(21 citation statements)

References 30 publications

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“…e matched data are extracted by short-time Fourier transform [21]. e calculation [22] process of short-time Fourier transform is regarded as a long-time signal segmentation and signal segmentation in the same time period, and its calculation formula is…”

Section: Realization Of Matching Error Recognition Based On Datamentioning

confidence: 99%

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Zhu

2022

Security and Communication Networks

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Aiming at the problems of low error recognition accuracy and low correlation of data matching in the existing dance action and music beat matching error recognition methods, a dance action and music beat matching error recognition method based on data mining is designed. The quaternion array is used to represent the joint coordinate points of the dance movement, and the coordinates are rotated to obtain the curve change of the dance movement. The dance movement characteristics are regarded as a set of positive and negative sample data, the initial weights of different sample data are calculated, the absolute value is processed for the music beat signal, the interference factors are filtered with the help of Gauss filter, and according to the rhythm law of the music beat signal, the characteristics of music beat signal are extracted and the feature extraction of dance action matching is completed with music beat. The corresponding relationship between dance action and music beat is regarded as the corresponding model, the pairwise occurrence probability between music beat and action is determined, the matching model between dance action and music beat is discretized, and the peak point of correlation data is introduced to complete the matching between dance action and music beat. The features of matching data are extracted and segmented by short-time Fourier transform, the segmented matching data is transformed into matching data, the matching error identification model is established with the help of support vector mechanism, and the constraint conditions of error detection are set to complete the matching error identification. The experimental results show that the proposed method has high recognition accuracy and high data matching correlation.

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Section: Realization Of Matching Error Recognition Based On Datamentioning

confidence: 99%

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Zhu

2022

Security and Communication Networks

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“…e training part of optimized convolutional neural network mainly includes prediction subnetwork and identification subnetwork of target region, which share convolutional neural network structure [19].…”

Section: Basketball Goal Recognition Based On Improvedmentioning

confidence: 99%

Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm

Hang-sheng

2021

Security and Communication Networks

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This paper studies the basketball goal recognition method based on image processing and improved algorithm to improve the accuracy of automatic recognition of basketball goal. The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by using the adaptive filtering algorithm, the wavelet analysis method is used to extract the features of basketball goal signal, which are input into the optimized deformable convolution neural network. Through the weighted sum of the values of each sampling point and the corresponding position authority of the block convolution core, the results are output as convolution operation. Combined with the depth feature of the same dimension, the full connection feature of the candidate target area is obtained to realize the basketball goal recognition. The experimental results show the following: the method can effectively identify basketball goals and the recognition error rate is low; the average accuracy of the automatic recognition results of basketball goals is as high as 98.4%; under the influence of different degrees of noise, the method is less affected by noise and has strong anti-interference ability.

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“…To sum up, it can be seen that the current volleyball video analysis system and the traditional image recognition model generally have the problems of poor recognition effect, low recognition accuracy, poor stability, and low data utilization in volleyball video analysis [ 18 , 19 ]. On the other hand, in the existing volleyball video analysis system, the vast majority of volleyball video intelligent recognition methods can only recognize a single volleyball video information and can not distinguish volleyball sports with obvious differences, so they do not have intelligent characteristics [ 20 – 22 ].…”

Section: State Of the Artmentioning

confidence: 99%

Analysis of Volleyball Video Intelligent Description Technology Based on Computer Memory Network and Attention Mechanism

Zhang

2021

Computational Intelligence and Neuroscience

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There are some problems in the process of video intelligent description and analysis of volleyball, such as poor effective information extraction rate and poor dynamic tracking effect. Based on this, combined with long-term and short-term memory network and attention mechanism, this paper designs an intelligent description model of volleyball video based on deep learning algorithm and studies how to improve the extraction rate of volleyball video information through intelligent detection hardware and image recognition technology. This paper first introduces the application of image recognition technology and deep learning algorithm in the intelligent description of volleyball video, then designs the volleyball video and image recognition model based on deep learning algorithm according to the requirements of volleyball video intelligent description, and selects three correlation factors related to the impact indicators of volleyball skills. This study selects three characteristic parameters associated with volleyball video analysis indexes, namely, take-off, bounce, and hand movement, combined with image sensing hardware assisted sensor network to realize real-time monitoring of action state in volleyball video analysis system. The experimental results show that, compared with the current mainstream sports video intelligent analysis and image recognition methods with data analysis as the core, the intelligent volleyball sports video intelligent description and image recognition system based on the integration of deep learning algorithm and sensor hardware assistance has the advantages of good detection effect, high data effectiveness, low cost, and high efficiency of volleyball sports video analysis. It can effectively improve the efficiency of volleyball video intelligent description.

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Image recognition model based on deep learning for remaining oil recognition from visualization experiment

Cited by 58 publications

References 30 publications

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm

Analysis of Volleyball Video Intelligent Description Technology Based on Computer Memory Network and Attention Mechanism

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