Research on Infrared Image Recognition Method of Power Equipment Based on Deep Learning

2020 ◽  
Author(s):  
Anfeng Jiang ◽  
Nannan Yan ◽  
Baoguo Shen ◽  
Chunjie Gu ◽  
Huangru Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Infrared Image ◽  
Power Equipment ◽  
Recognition Method

scholarly journals Research on Robustness of Related Images for Power Equipment Inspection

2021 ◽  
Vol 2113 (1) ◽  
pp. 012045
Author(s):  
Chunlei Zhou ◽  
Xiangzhou Chen ◽  
Wenli Liu ◽  
Tianyu Dong ◽  
Huang Yun
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Feature Fusion ◽  
Power Equipment ◽  
Actual Measurement ◽  
Practical Applications ◽  
Detection Algorithms ◽  
Traction Substations ◽  
Deep Learning Model ◽  
Selection Of

Abstract With the increase in the number of traction substations year by year, manual inspections are gradually being replaced by unattended inspections. Target detection algorithms based on deep learning are more widely used in intelligent inspections of power equipment. However, in practical applications, it is found that due to the small target to be detected, the accuracy of the deep learning model will decrease when the shooting angle is inclined and the light conditions are poor. This is because the algorithm’s robustness is low, and the detection ability of the model will be seriously affected when the angle or illumination difference with the sample is large. Based on this, the feature fusion part of the YOLOv3 algorithm and the selection of the loss function and the size of the anchor frame are improved, and the improved ASFF fusion method is used to classify various images in the power equipment. Actual measurement and repeated experiments show that the proposed method can be effectively applied to image recognition of various power equipment, optimize robustness, and greatly improve the image recognition efficiency of power equipment.

CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network

2020 ◽  
Vol 37 (9) ◽  
pp. 1661-1668
Author(s):  
Min Wang ◽  
Shudao Zhou ◽  
Zhong Yang ◽  
Zhanhua Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Learning Ability ◽  
Visualization Method ◽  
Classification Methods ◽  
Recognition Method ◽  
Cloud Classification ◽  
Average Accuracy

AbstractConventional classification methods are based on artificial experience to extract features, and each link is independent, which is a kind of “shallow learning.” As a result, the scope of the cloud category applied by this method is limited. In this paper, we propose a new convolutional neural network (CNN) with deep learning ability, called CloudA, for the ground-based cloud image recognition method. We use the Singapore Whole-Sky Imaging Categories (SWIMCAT) sample library and total-sky sample library to train and test CloudA. In particular, we visualize the cloud features captured by CloudA using the TensorBoard visualization method, and these features can help us to understand the process of ground-based cloud classification. We compare this method with other commonly used methods to explore the feasibility of using CloudA to classify ground-based cloud images, and the evaluation of a large number of experiments show that the average accuracy of this method is nearly 98.63% for ground-based cloud classification.

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.

Intelligent grading system based on deep learning

2021 ◽  
pp. 002072092098399
Author(s):  
Meng Xiao ◽  
Haibo Yi
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Secondary Schools ◽  
Image Recognition ◽  
Processing Method ◽  
Grading System ◽  
Recognition Method ◽  
Lightweight Framework ◽  
Examination Method ◽  
Learning Techniques

According to the survey, off-line examination is still the main examination method in universities, primary and secondary schools. The grading processing of off-line examination is time-consuming. Besides, since the off-line grading is subjective, it is error-prone. In order to address the challenges in off-line examinations of universities, primary and secondary schools, it is very urgent to improve the efficiency of off-line grading. In order to realize intelligent grading for off-line examinations, we exploit deep learning techniques to off-line grading. First, we propose an image processing method for English letters. Second, we propose a image recognition method based on deep learning for English letters. Third, we propose a lightweight framework for grading. Based on the above designs, we design an intelligent grading system based on deep learning. We implement the system and the result shows that the intelligent grading system can batch grading efficiently. Besides, compared with related designs, the proposed system is more flexible and intelligent.

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