Digital Substation Measurement Image Recognition Method Based on Improved Deep Learning Algorithm

2021 ◽  
Vol 11 (03) ◽  
pp. 561-570
Author(s):  
超 丁
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Learning Algorithm ◽  
Recognition Method ◽  
Deep Learning Algorithm ◽  
Digital Substation

Progressive Image Recognition Method and Its Application in Security In-spection Machines

2020 ◽  
Vol 01 ◽  
Author(s):  
Wu Jianxing ◽  
Zeng Dexin ◽  
Ju Qiaodan ◽  
Chang Zixuan ◽  
Yu Hai
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Learning Algorithm ◽  
Local Information ◽  
Feature Mapping ◽  
Recognition Method ◽  
Deep Learning Algorithm ◽  
Detection Technology ◽  
Security Inspection ◽  
Security Inspection Equipment

Background:: Owing to the ability of a deep learning algorithm to identify objects and the related detection technology of security inspection equipment, in this paper, we propose a progressive object recognition method that con-siders local information of objects. Methods:: First, we construct an X-Base model by cascading multiple convolutions and pooling layers to obtain the feature mapping image. Moreover, we provide a “segmented convolution, unified recognition” strategy to detect the size of the objects. Results:: Experimental results show that this method can effectively identify the specifications of bags passing through the security inspection equipment. Compared with the traditional VGG and progressive VGG recognition methods, the pro-posed method achieves advantages in terms of efficiency and concurrency. Conclusion:: This study provides a method to gradually recognize objects and can potentially assist the operators to identify prohibited objects.

scholarly journals Augmented Reality Dynamic Image Recognition Technology Based on Deep Learning Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 137370-137384 ◽  
Author(s):  
Qiuyun Cheng ◽  
Sen Zhang ◽  
Shukui Bo ◽  
Dengxi Chen ◽  
Haijun Zhang
Keyword(s):  
Deep Learning ◽  
Augmented Reality ◽  
Image Recognition ◽  
Learning Algorithm ◽  
Dynamic Image ◽  
Deep Learning Algorithm

scholarly journals utomated Skin Disease Identification using Deep Learning Algorithm

2018 ◽  
Vol 11 (3) ◽  
pp. 1429-1436 ◽  
Author(s):  
Sourav Kumar Patnaik ◽  
Mansher Singh Sidhu ◽  
Yaagyanika Gehlot ◽  
Bhairvi Sharma ◽  
P. Muthu
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Skin Disease ◽  
Skin Diseases ◽  
Learning Algorithm ◽  
Disease Prediction ◽  
Learning Technology ◽  
Deep Learning Algorithm ◽  
Tone Color ◽  
Dermatological Disorders

Dermatological disorders are one of the most widespread diseases in the world. Despite being common its diagnosis is extremely difficult because of its complexities of skin tone, color, presence of hair. This paper provides an approach to use various computer vision based techniques (deep learning) to automatically predict the various kinds of skin diseases. The system uses three publicly available image recognition architectures namely Inception V3, Inception Resnet V2, Mobile Net with modifications for skin disease application and successfully predicts the skin disease based on maximum voting from the three networks. These models are pretrained to recognize images upto 1000 classes like panda, parrot etc. The architectures are published by image recognition giants for public usage for various applications. The system consists of three phases- The feature extraction phase, the training phase and the testing /validation phase. The system makes use of deep learning technology to train itself with the various skin images. The main objective of this system is to achieve maximum accuracy of skin disease prediction.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhongzi Zhang
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Video Analysis ◽  
Learning Algorithm ◽  
Attention Mechanism ◽  
Extraction Rate ◽  
Sports Video ◽  
Deep Learning Algorithm ◽  
Memory Network ◽  
The Impact

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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