Machine Vision System Research Topics

A machine vision (MV) system research topic is   now utilized in various applications. It utilizes cameras to capture visual information from the surrounding locations. Here we provide some information related to the MV Technology.

1. Define Machine Vision Technology

At the first stage we look into the definition of MV system. It utilizes cameras to seizure the visual details from the nearby surroundings. Then images are processed by several software and hardware which makes the detail for utilizing different applications. Machine Vision Technology frequently utilizes particular view to obtain images. Its architecture has five major components namely lens, communications, lighting, vision processing and image sensor.

2. What is Machine Vision system?

After the definition of MV system we see the detailed explanation of this. It is the technology that allows robots and other machines like autonomous vehicles to notice and identify the objects in their nearby location. Combining optic sensors with machine learning and artificial intelligence apparatus which examine and process image data, autonomous vehicles and robots prepared with machine vision systems have the ability to achieve more difficult tasks such as dragging objects in a warehouse or navigating downtown traffic.

Types of Machine Vision system

There are different types of Machine Vision system are available. Some of the possible Machine Vision Technology types are Hyperspectral imaging, 3D vision system, Variable magnification issues, Smart camera-based vision systems, PC-based Vision systems, 2D vision system, Compact Vision System and the Multispectral Imaging

3. Where Machine Vision system used?

Next to the deep explanation of Machine Vision system we discuss where to use this. The most general utilization of Machine Vision Technology is defect detection and visual inspection, measuring parts and positioning, and sorting, analyzing and locating products.

4. Why Machine Vision system proposed? , previous technology issues

The MV system is proposed in this work and it overcomes some existing technology issues. Some of the previous technology issues are speed, quality control, automation and efficiency, non-invasive inspection, accuracy and consistency, data analysis, real-time decision-making and cost analysis. Machine Vision Technology is proposed and accepted in different industries and applications for some reasons, frequently to overcome the limitations and difficulties posed by existing technique and methods. This technology has the possibility to considerably increase efficiency and automation, accuracy and consistency, speed, quality control, non-invasive inspection, cost savings, data analysis and real-time decision-making.

5. Algorithms / protocols

In this research the Machine Vision system is proposed and it overcomes the problems in existing research. Here we utilize several algorithms/methods for Machine Vision Technologies are You Look Only Once (YOLO-V7), Multi Agent Deep Q Learning (MA-DQL), Putrefaction Enrichment and Tuning Network (PETNet), Gazelle Optimization Algorithm (GOA), Skipped Attention Gated Recurrent Unit and Siamese Network based Kalman Filter (SNNKF).

6. Comparative study / Analysis

Succeeding the algorithms/protocols to be utilized in the proposed work, next we discuss the comparative study for this research; in this we compare various methods to obtain the correct possible outcome:

• YOLO-V7 is the new version of YOLO object detection method. It is a single-stage detection method and it has the capacity to identify multiple objects in a single image.
• Multi-agent deep Q-learning (MADQL) is a reinforcement learning method that permits multiple agents to acquire own knowledge and communications in a shared environment.
• GOA is a global stochastic optimizer that is straightforward and has robust search ability. It needs a deep examination to increase its search ability and overcomes different multimodal-hybrid functions and data mining problems effectively.

7. Simulation results / Parameters

For Machine Vision system we compared various parameters to obtain the possible outcome. The parameters that we compared are F1 score, Precision, Recall, and Accuracy. The parameter grade is compared with recovery and for the confusion matrix Actual working condition with Detected working time and Actual grade recovery with prediction grade recovery are compared. These are the parameters that we compared to get the best finding.

Now we consider our proposed work simulation. The proposed work is simulated by Simulation of Urban Mobility (SUMO) and Objective Modular Network (OMNET ++) simulation apparatus, in that the ++ indicates the OMNET surroundings utilized C++ for testing.

8. Dataset LINKS / Important URL

Below we provide some important links that are very useful to know the details about Machine Vision Technology. The link gives some information about Machine Vision Technology that is helpful to notice the details of this technology:

• https://doi.org/10.1007/s42461-023-00768-4
• https://doi.org/10.1016/j.apsadv.2022.100287
• https://doi.org/10.3390/pr10101933
• https://doi.org/10.1080/00207543.2021.1894366
• https://doi.org/10.26599/TST.2019.9010071

9. Machine Vision system Applications

Here we proposed the Machine Vision system that is broadly utilized in many applications. Some of the applications for Machine Vision Technology are Remote sensing and earth observation, environmental remediation, Agriculture and precision farming, Renewable energy, Wildlife conservation, weather monitoring, Environmental monitoring, , Natural resource management and Ecological research.

10. Topology for Machine Vision system

Now we see the topology for Machine Vision system, some of the topologies we used are embedded vision, Hyperspectral imaging, Multi-camera system, cloud-based vision and thermal imaging.

11. Environment in Machine Vision system

In a broad environment, the Machine Vision system is executed and relying on the application needs. Its adjustability and flexibility create it apt for different settings, indoor as well as outdoor. It performs a significant role in quality control, safety, automation, and data analysis through a broad range of sectors and industries.

12. Simulation tools

The MV Technology is proposed in this work and it overcome some previous technology issues by utilizing the succeeding simulation tools. This technology is developed by using the tools like Omnet++ and SUMO 0.25. And then it is implemented by using the language C++ and the environment it utilized for system is windows 10 [64-bit].

13. Results

In this research the Machine Vision system is proposed, it is broadly used in many applications. We also utilize this to obtain clear findings by implementing it in a C++ language. By comparing the various parameters to this research and obtain the clear findings to this research.

Machine Vision system Research Ideas:

            Following are some of the research topics related to the Machine Vision system, which is employed to provide us some information or descriptions about this technology:

1. Research on machine vision technology based detection and tracking of objects on video image
2. Design of Wireless Site-Specific Spraying System Based on Machine Vision Technology
3. Research on the Application of Computer Machine Vision Technology in the Electrical Automation of New Energy Vehicles
4. Robot machine vision and deep learning technology for pipeline safety management in military ships
5. Application of Machine Vision Technology in 3D Printing Quality Inspection System
6. Development and Design of Tennis Obstacle Avoidance System Based on Machine Vision Technology
7. Raising Awareness of Driverless Cars at Closed Intersections Using Urban Infrastructure, Machine Vision Technologies and V2X Sharing
8. Research on 3D visualization technology of electromagnetic source arc based on machine vision
9. Intelligent Real-Time Image Processing Technology of Badminton Robot via Machine Vision and Internet of Things
10. Lane line detection technology based on machine vision
11. Electric Vehicle Alignment Guidance Technology Based on the Fusion of Machine Vision and Magnetic Field Information
12. An overview of power supply technologies for Unmanned Aerial Vehicles (UAVs) and machine vision applications
13. A novel method for seed cotton color measurement based on machine vision technology
14. Representations of machine vision technologies in artworks, games and narratives: A dataset
15. Online defect detection method of optical cable pitch based on machine vision technology and deep learning algorithms
16. Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology
17. Key technologies of machine vision for weeding robots: A review and benchmark
18. Tracking pecking behaviors and damages of cage-free laying hens with machine vision technologies
19. Detecting sow vulva size change around estrus using machine vision technology
20. Retraction Note: Research on multi-target tracking technology based on machine vision
21. Image Edge Enhancement Detection Method of Human-Computer Interaction Interface Based on Machine Vision Technology
22. Research on multi-target tracking technology based on machine vision
23. Algorithms and Systems of Machine Vision in Integrated Circuit Manufacturing Technology
24. Research on the Positioning Technology of Sports 3D Teaching Action Based on Machine Vision
25. Application and effect simulation of image recognition technology based on machine vision feature parameters in art teaching
26. Extraction of landslide features in UAV remote sensing images based on machine vision and image enhancement technology
27. Control of adaptive running platform based on machine vision technologies and neural networks
28. Research on Cable Sampling Method based on Machine Vision Algorithm
29. Review of the Application of Machine Vision in Aquaculture UAVs
30. Mobile robot-integrated machine vision and RFID systems for improving fire safety in care environments
31. Basic Research on Machine Vision Underpinned by Image Frame Algebra (VFA) and Visual Semantic Algebra (VSA)
32. Engineering Applications based on Industrial Robots and Machine Vision
33. Identification Method of Dress Pattern Drawing based on Machine Vision Algorithm
34. Design Flow of mmWave Radar and Machine Vision Fusion for Pedestrian Collision Warning
35. Intelligent Human-machine Game System Based on Machine Vision
36. Design and Implementation of Machine Vision Experiment Platform for Virtual Production Line
37. A Flexible Machine Vision AI System for Edge-Oriented Deep Learning Accelerators
38. Design of Industrial Machine Vision System Based on Improved Genetic Algorithm
39. Validation of a Mobile Robot-Integrated RFID and Machine Vision System for Elderly Care Environments
40. 3D Machine Vision System for Defect Inspection and Robot Guidance
41. A Household Garbage Classification and Collection Device Based on Machine Vision and Deep Learning
42. K-Decision Tree Control Method of Welding Robots based on Machine Vision
43. Feature Structure Similarity Index for Hybrid Human and Machine Vision
44. Machine Vision based automated 3-DOF Articulated Robot for fruit defect Identification and Segregation
45. Application Research of Machine Vision Platform Based on Deep Neural Network and Software Engineering
46. Bank intelligent auxiliary management system based on machine vision and facial recognition
47. Visible Light Intelligent Inspection of Transmission Line UAV Based on Machine Vision
48. Audience Behavior Analysis System Design for Outdoor Advertising based on Machine Vision
49. A Spot Welding Spatter Monitoring System Based on Machine Vision
50. Exploration of Machine Vision Curriculum Construction Facing on Industry Demand and Talent Training Demand of Higher Education