Feature recognition and detection for ancient architecture based on machine vision

An Automatic Detection and Online Quality Inspection Method for Workpiece Surface Cracks based on Machine Vision

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2022.16.18 ◽

2022 ◽

Vol 16 ◽

pp. 141-150

Author(s):

Cuili Mao ◽

Wen Ma

Keyword(s):

Machine Vision ◽

Feature Recognition ◽

Automatic Detection ◽

Calibration Method ◽

Specific Method ◽

Quality Inspection ◽

Surface Cracks ◽

Feature Maps ◽

Inspection Method ◽

Workpiece Surface

The wide application of intelligent manufacturing technologies imposes higher requirements for the quality inspection of industrial products; however, the existing industrial product quality inspection methods generally have a few shortcomings such as requiring many inspectors, too complicated methods, difficulty in realizing standardized monitoring, and the low inspection efficiency, etc. Targeting at these problems, this paper proposed an automatic detection and online quality inspection method for workpiece surface cracks based on the machine vision technology. At first, it proposed a vision-field environment calibration method, gave the specific method for workpiece shape feature recognition and size measurement based on machine vision, and achieved the on-line monitoring of workpiece quality problems such as feature defects and size deviations. Then, this study integrated the multi-scale attention module and the up-sampling module that can restore the locations of image pixels based on the high-level and low-level hybrid feature maps, built a workpiece crack extraction network, and realized workpiece crack feature extraction, crack type classification, and damage degree division. At last, experimental results verified the effectiveness of the proposed method, and this paper provided a reference for the application of machine vision technology in other fields.

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Research on Character Computer Intelligent Recognition of Automobile Front Bar Bracket Based on Machine Vision

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/4/042041 ◽

2021 ◽

Vol 2083 (4) ◽

pp. 042041

Author(s):

Chuanxing Shen ◽

Yongjian Zhu ◽

Chi Wang

Keyword(s):

Machine Vision ◽

Character Recognition ◽

Feature Recognition ◽

Domain Size ◽

Color Difference ◽

Support Vector ◽

Character Feature ◽

Opening And Closing ◽

Intelligent Recognition ◽

Stereo Algorithm

Abstract Aiming at the problem that the characters in the superimposed character area on the surface of the front bumper bracket of the car are difficult to recognize, a machine vision-based automobile front bumper bracket character recognition method is proposed, use Python, OpenCV and Halcon computer vision library for software system design. For eight images collected from different angles of light source directions, an innovative method of polynomial fitting gray value can reducethe uneven illumination of the images. The photometric stereo algorithm is used to obtain a high-contrast character image, and the separation of the two types of characters is simultaneously achieved. After the image filtering, opening and closing processes canremove background interference, use a morphological improvement algorithm based on scanning algorithm to complete character positioning, and then the improved algorithm of projection dichotomy based on the connected domain size is used to complete the character segmentation, and finally the support vector machine is used for character feature recognition. The experimental results claim that these methods can quickly separate superimposed characters and complete the recognition of non-color difference convex characters and inkjet characters, and the average recognition accuracy rate is over 96%, which meets the expected recognition requirements.

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Dimentional inspection and feature recognition through machine vision system

2010 IEEE 17Th International Conference on Industrial Engineering and Engineering Management ◽

10.1109/icieem.2010.5646564 ◽

2010 ◽

Author(s):

B. K. Bhattacharyya

Keyword(s):

Machine Vision ◽

Feature Recognition ◽

Vision System ◽

Machine Vision System

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Machine Vision

10.1017/cbo9781139168229 ◽

2009 ◽

Cited By ~ 15

Author(s):

Wesley E. Snyder ◽

Hairong Qi

Keyword(s):

Machine Vision

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Design of English text-to-speech conversion algorithm based on machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189238 ◽

2020 ◽

pp. 1-12

Author(s):

Li Dongmei

Keyword(s):

Machine Learning ◽

Speech Synthesis ◽

Feature Recognition ◽

Learning Algorithm ◽

Morphological Structure ◽

English Text ◽

Text To Speech ◽

Part Of Speech ◽

Modern Computer ◽

Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

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Recognition of classroom student state features based on deep learning algorithms and machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189232 ◽

2020 ◽

pp. 1-12

Author(s):

Hu Jingchao ◽

Haiying Zhang

Keyword(s):

Deep Learning ◽

Feature Recognition ◽

Subjective Evaluation ◽

Recognition Algorithm ◽

Video Data ◽

State Recognition ◽

Detection Model ◽

State Classification ◽

Intelligent Models ◽

Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

Light & Engineering ◽

10.33383/2018-135 ◽

2018 ◽

pp. 143-149 ◽

Cited By ~ 1

Author(s):

Ruijie CHENG

Keyword(s):

Control System ◽

Machine Vision ◽

Energy Saving ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Lighting Control ◽

Lighting Energy ◽

Neural Network Algorithm ◽

Energy Saving Control ◽

Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multifeedback model for energysaving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

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