Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning
In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/3/984 |
| _version_ | 1827354555970486272 |
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| author | Rong Hou Jianping Yin Yanchen Liu Huijuan Lu |
| author_facet | Rong Hou Jianping Yin Yanchen Liu Huijuan Lu |
| author_sort | Rong Hou |
| collection | DOAJ |
| description | In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot’s work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand–eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs. |
| first_indexed | 2024-03-08T03:49:22Z |
| format | Article |
| id | doaj.art-fc99cce388d544faad023dfc51dfafde |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:49:22Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fc99cce388d544faad023dfc51dfafde2024-02-09T15:22:29ZengMDPI AGSensors1424-82202024-02-0124398410.3390/s24030984Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep LearningRong Hou0Jianping Yin1Yanchen Liu2Huijuan Lu3School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, ChinaIn the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot’s work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand–eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.https://www.mdpi.com/1424-8220/24/3/984machine visiondeep learningrobotic armhand–eye calibrationROS communicationsporous disk |
| spellingShingle | Rong Hou Jianping Yin Yanchen Liu Huijuan Lu Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning Sensors machine vision deep learning robotic arm hand–eye calibration ROS communications porous disk |
| title | Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning |
| title_full | Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning |
| title_fullStr | Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning |
| title_full_unstemmed | Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning |
| title_short | Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning |
| title_sort | research on multi hole localization tracking based on a combination of machine vision and deep learning |
| topic | machine vision deep learning robotic arm hand–eye calibration ROS communications porous disk |
| url | https://www.mdpi.com/1424-8220/24/3/984 |
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