scholarly journals Research on Image Recognition of Electrical Equipment based on Deconvolution Feature Extraction

2021 ◽  
Vol 257 ◽  
pp. 01019
Author(s):  
Zhe Li ◽  
Haifeng Su
Keyword(s):  
Machine Learning ◽  
Recognition Accuracy ◽  
Image Data ◽  
Electrical Equipment ◽  
Learning Technology ◽  
Infrared Images ◽  
Circuit Breakers ◽  
Dual Mode ◽  
Weighted Fusion ◽  
Improved Model

Based on machine learning technology and combining the operation of machine learning from the idea of neural network, this paper focuses on the classification and recognition of image data of transformers, circuit breakers and isolation switches in substations. Firstly, the image enhancement is carried out on the basis of the original image, which simulates the possible scenes in reality. Secondly, using the dual-mode a deconvolutional network to capture significant features from in-depth visible and infrared images. Furthermore, all these features are subjected to the program to conduct transfer learning and weighted fusion. The dual-mode deconvolutional network (DMDN) extracts and highlights the features of the electrical equipment. Compared to traditional model, the recognition accuracy of the improved model is reached at 99.17%.

scholarly journals AI-based targeted advertising system

2019 ◽  
Vol 13 (2) ◽  
pp. 787
Author(s):  
Tew Jia Yu ◽  
Chin Poo Lee ◽  
Kian Ming Lim ◽  
Siti Fatimah Abdul Razak
Keyword(s):  
Machine Learning ◽  
Recognition Accuracy ◽  
Facial Recognition ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Outdoor Advertising ◽  
Advertising Effect ◽  
Targeted Advertising ◽  
Vehicle Recognition ◽  
Gender And Age

<span>The most common technology used in targeted advertising is facial recognition and vehicle recognition. Even though there are existing systems serving for the targeting purposes, most propose limited functionalities and the system performance is normally unknown. This paper presents an intelligent targeted advertising system with multiple functionalities, namely facial recognition for gender and age, vehicle recognition, and multiple object detection. The main purpose is to improve the effectiveness of outdoor advertising through biometrics approaches and machine learning technology. Machine learning algorithms are implemented for higher recognition accuracy and hence achieved better targeted advertising effect.</span>

Accuracy analysis of Japanese machine translation based on machine learning and image feature retrieval

2020 ◽  
pp. 1-12
Author(s):  
Gang Song
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Character Recognition ◽  
Image Data ◽  
Image Features ◽  
Image Feature ◽  
Learning Technology ◽  
Japanese Character ◽  
Knowledge Support ◽  
Translation Methods

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research.

scholarly journals Digital Audio Scene Recognition Method Based on Machine Learning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Sihua Sun
Keyword(s):  
Machine Learning ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Scene Recognition ◽  
Learning Technology ◽  
Digital Audio ◽  
Recognition Method ◽  
Feature Extraction Method ◽  
Auditory Scene ◽  
Experimental Data Analysis

Audio scene recognition is a task that enables devices to understand their environment through digital audio analysis. It belongs to a branch of the field of computer auditory scene. At present, this technology has been widely used in intelligent wearable devices, robot sensing services, and other application scenarios. In order to explore the applicability of machine learning technology in the field of digital audio scene recognition, an audio scene recognition method based on optimized audio processing and convolutional neural network is proposed. Firstly, different from the traditional audio feature extraction method using mel-frequency cepstrum coefficient, the proposed method uses binaural representation and harmonic percussive source separation method to optimize the original audio and extract the corresponding features, so that the system can make use of the spatial features of the scene and then improve the recognition accuracy. Then, an audio scene recognition system with two-layer convolution module is designed and implemented. In terms of network structure, we try to learn from the VGGNet structure in the field of image recognition to increase the network depth and improve the system flexibility. Experimental data analysis shows that compared with traditional machine learning methods, the proposed method can greatly improve the recognition accuracy of each scene and achieve better generalization effect on different data.

scholarly journals A Violation Information Recognition Method of Live-Broadcasting Platform Based on Machine Learning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoying Shen ◽  
Chao Yuan
Keyword(s):  
Machine Learning ◽  
Skin Color ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Learning Technology ◽  
Recognition Method ◽  
Data Set ◽  
Color Recognition ◽  
Live Broadcast ◽  
Information Recognition

With the development of the live broadcast industry, security issues in the live broadcast process have become increasingly apparent. At present, the supervision of various live broadcast platforms is basically in a state of human supervision. Manpower supervision is mainly through user reporting and platform supervision measures. However, there are a large number of live broadcast rooms at the same time, and only relying on human supervision can no longer meet the monitoring needs of live broadcasts. Based on this situation, this study proposes a violation information recognition method of a live-broadcasting platform based on machine learning technology. By analyzing the similarities and differences between normal live broadcasts and violation live broadcasts, combined with the characteristics of violation image data, this study mainly detects human skin color and sensitive parts. A prominent feature of violation images is that they contain a large area of naked skin, and the ratio of the area of naked skin to the overall image area of the violation image will exceed the threshold. Skin color recognition plays a role in initial target positioning. The accuracy of skin color recognition is directly related to the recognition accuracy of the entire system, so skin color recognition is the most important part of violation information recognition. Although there are many effective skin color recognition technologies, the accuracy and stability of skin color recognition still need to be improved due to the influence of various external factors, such as light intensity, light source color, and physical equipment. When it is detected that the area of the skin color in the live screen exceeds the threshold, it is preliminarily determined to be a suspected violation video. In order to improve the recognition accuracy, it is necessary to detect sensitive parts of the suspected video. Naked female breasts are a very obvious feature in violation images. This study uses a chest feature extraction method to detect the chest in the image. When the recognition result is a violation image, it is determined that the live broadcast involves violation content. The machine learning algorithm is simple to implement, and the parameters are easy to adjust. The classifier training requires a short time and is suitable for live violation information recognition scenarios. The experimental results on the adopted data set show that the method used in this article can effectively detect videos with violation content. The recognition rate is as high as 85.98%, which is suitable for a real-life environment and has good practical significance.

scholarly journals Research and application of bolt defects detection technology based on machine learning

2021 ◽  
Vol 2085 (1) ◽  
pp. 012012
Author(s):  
Zhidong Yao ◽  
Jiaqi Lu ◽  
Yesen Liu ◽  
Gang Wang
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Embedded System ◽  
High Performance ◽  
Image Data ◽  
Steel Structure ◽  
Learning Technology ◽  
Detection Technology ◽  
Training Samples ◽  
High Recognition Accuracy

Abstract With the development of computer technology, the technology based on computer machine learning plays an important role in various fields. Using drones for collecting image data and using machine learning to analyze the collected image data have become the current general method of intelligent detection technology. As the main machine learning method, deep learning is commonly used in image analysis, but it requires many high-quality training samples and high-performance embedded system. In the engineering quality and safety detection with few training samples, the detection effect of this method is not satisfactory. To solve this problem, computer vision and machine learning technology are introduced into image analysis of bolt, based on the analysis and mining of historical image samples, the recognition and judgment of new collected images can be realized by matching the newly collected image samples and historical samples. Taking the bolt on a steel structure bridge as an example, this method is used to recognize the bolt appearance image collected by UAV. The results show that the method can effectively identify the appearance state of bolts, with fast calculation speed and high recognition accuracy.

EFFICIENCY IMPROVEMENT OF ROAD SURFACE INSPECTION USING MACHINE LEARNING TECHNOLOGY

2017 ◽  
Vol 73 (2) ◽  
pp. I_409-I_415
Author(s):  
Naoko FUKUSHI ◽  
Daishiro KOBAYASHI ◽  
Seiji IWAO ◽  
Ryosuke KASAHARA ◽  
Nobuyoshi YABUKI
Keyword(s):  
Machine Learning ◽  
Road Surface ◽  
Surface Inspection ◽  
Learning Technology ◽  
Efficiency Improvement

scholarly journals PCANet Based Digital Recognition for Electrical Equipment Infrared Images

2018 ◽  
Vol 1098 ◽  
pp. 012033
Author(s):  
Ying Lin ◽  
Jiafeng Qin ◽  
Weiwei Zhang ◽  
Hao Zhang ◽  
Demeng Bai ◽  
...  
Keyword(s):  
Electrical Equipment ◽  
Infrared Images

scholarly journals Automobile tire life prediction based on image processing and machine learning technology

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Jianchen Zhu ◽  
Kaixin Han ◽  
Shenlong Wang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Life Prediction ◽  
Traffic Accidents ◽  
Confusion Matrix ◽  
Small Sample ◽  
Image Feature ◽  
Engineering Practice ◽  
Learning Technology ◽  
K Nearest Neighbor

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

Color Trend Analysis using Machine Learning with Fashion Collection Images

2021 ◽  
pp. 0887302X2199594
Author(s):  
Ahyoung Han ◽  
Jihoon Kim ◽  
Jaehong Ahn
Keyword(s):  
Machine Learning ◽  
Trend Analysis ◽  
Image Data ◽  
Fashion Industry ◽  
Color Palette ◽  
Web Scraping ◽  
Design Variables ◽  
Sales Organizations ◽  
Fashion Designers ◽  
Selection Of

Fashion color trends are an essential marketing element that directly affect brand sales. Organizations such as Pantone have global authority over professional color standards by annually forecasting color palettes. However, the question remains whether fashion designers apply these colors in fashion shows that guide seasonal fashion trends. This study analyzed image data from fashion collections through machine learning to obtain measurable results by web-scraping catwalk images, separating body and clothing elements via machine learning, defining a selection of color chips using k-means algorithms, and analyzing the similarity between the Pantone color palette (16 colors) and the analysis color chips. The gap between the Pantone trends and the colors used in fashion collections were quantitatively analyzed and found to be significant. This study indicates the potential of machine learning within the fashion industry to guide production and suggests further research expand on other design variables.

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