Fast-axis collimating lens recognition algorithm based on machine vision

Abstract. Tobacco grading is the first step in the transfer of tobacco leaves from agricultural products to commodities and is key to determining the quality of tobacco. Manual grading is conventionally used for tobacco grading. However, it is time-consuming, expensive, and may require specialized labor. To overcome these limitations, a method for grade identification of tobacco leaves based on machine vision is proposed in this article. Based on a fuzzy pattern recognition algorithm, the tobacco leaf samples of the model set and prediction set could be classified by extracting appearance characteristics of the tobacco leaves. The identification system for tobacco leaves based on fuzzy pattern recognition was developed in MATLAB. The rate of correct grading was 85.81% and 80.23% for the modeling set and prediction set, respectively. This result shows that machine vision based automatic tobacco grading has a great advantage over manual grading, and this method can be explored for viable commercial use. Keywords: Fuzzy pattern recognition, Grade identification, Machine vision, Tobacco leaf.

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Research on Pose Recognition Algorithm for Sports Players Based on Machine Learning of Sensor Data

Security and Communication Networks ◽

10.1155/2021/9327619 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Chunlong Zhang ◽

Hongtao He

Keyword(s):

Machine Vision ◽

Performance Test ◽

Original System ◽

Recognition System ◽

Recognition Algorithm ◽

Sensor Data ◽

Convergence Time ◽

Pose Tracking ◽

Convolution Algorithm ◽

Sequence Graph

The existing motion recognition system has a low athlete tracking recognition accuracy due to the poor processing effect of recognition algorithm for edge detection. A machine vision-based gymnast pose-tracking recognition system is designed for the above problem. The software part mainly optimizes the tracking recognition algorithm and uses the spatiotemporal graph convolution algorithm to construct the sequence graph structure of human joints, completes the strategy of label subset division, and completes the pose tracking according to the change of information dimension. The results of the system performance test show that the designed machine vision-based gymnast posture tracking recognition system can enhance the accuracy of tracking recognition and reduce the convergence time compared with the original system.

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Research on Container Door Handle Recognition Algorithm Based on Machine Vision

2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI) ◽

10.1109/issi.2018.8538159 ◽

2018 ◽

Author(s):

WANG Jun ◽

MI Weijian

Keyword(s):

Machine Vision ◽

Recognition Algorithm

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Intelligent Analysis of Core Identification Based on Intelligent Algorithm of Core Identification

Discrete Dynamics in Nature and Society ◽

10.1155/2021/5242930 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xiaolin Zhu ◽

Wei Lv

Keyword(s):

Machine Vision ◽

Mobile Phone ◽

Image Recognition ◽

Recognition Algorithm ◽

System Model ◽

Core Wire ◽

Intelligent Algorithm ◽

The Core ◽

Intelligent Analysis ◽

Depth Study

The communication recognition of mobile phone core is a test of the development of machine vision. The size of mobile phone core is very small, so it is difficult to identify small defects. Based on the in-depth study of the algorithm, combined with the actual needs of core identification, this paper improves the algorithm and proposes an intelligent algorithm suitable for core identification. In addition, according to the actual needs of core wire recognition, this paper makes an intelligent analysis of the core wire recognition process. In addition, this paper improves the traditional communication image recognition algorithm and analyzes the data of the recognition algorithm according to the shape and image characteristics of the mobile phone core. Finally, after constructing the functional structure of the system model constructed in this paper, the system model is verified and analyzed, and on this basis, the performance of the improved core recognition algorithm proposed in this paper is verified and analyzed. From the results of online monitoring and recognition, the statistical accuracy of mobile phone core video recognition is about 90%, which has higher accuracy in mobile phone core image recognition than traditional recognition algorithms. The core line recognition algorithm based on deep learning and machine vision is effective and has a good practical effect.

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Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs

Applied Sciences ◽

10.3390/app112110235 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10235

Author(s):

Heonmoo Kim ◽

Yosoon Choi

Keyword(s):

Machine Vision ◽

Autonomous Driving ◽

Recognition System ◽

Recognition Algorithm ◽

Underground Mines ◽

Driving Mode ◽

Road Signs ◽

Sign Recognition ◽

Road Sign ◽

Road Sign Recognition

In this study, an autonomous driving robot that drives and returns along a planned route in an underground mine tunnel was developed using a machine-vision-based road sign recognition algorithm. The robot was designed to recognize road signs at the intersection of a tunnel using a geometric matching algorithm of machine vision, and the autonomous driving mode was switched according to the shape of the road sign to drive the robot according to the planned route. The autonomous driving mode recognized the shape of the tunnel using the distance data from the LiDAR sensor; it was designed to drive while maintaining a fixed distance from the centerline or one wall of the tunnel. A machine-vision-based road sign recognition system and an autonomous driving robot for underground mines were used in a field experiment. The results reveal that all road signs were accurately recognized, and the average matching score was 979.14 out of 1000, confirming stable driving along the planned route.

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