Multi-angle automotive fuse box detection and assembly method based on machine vision

In the manual assembly of the blind area, the worker's line of sight is blocked, and the real-time state of the parts to be assembled cannot be seen, which has a great impact on the efficiency and accuracy of the assembly. Aiming at this problem, a blind zone assembly method based on machine vision and augmented reality(AR) is proposed. Firstly, the ellipse is used as the marker point. The object to be assembled in the blind area is indirectly tracked by the detection and positioning of the ellipse, and the AR visualization guide assembly is then performed by projection and the assembly is precisely guided using the principle of local error amplification. Finally, the blind zone assembly experiment based on machine vision and augmented reality is designed to verify the effectiveness of the method. The experimental results show that this method can significantly improve the efficiency of assembly work in blind areas and can effectively reduce the assembly error rate.

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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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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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Machine Vision, Let's Go into the Street

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss1987.117.10_1339 ◽

1997 ◽

Vol 117 (10) ◽

pp. 1339-1344

Author(s):

Katsuhiko Sakaue ◽

Hiroyasu Koshimizu

Keyword(s):

Machine Vision

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Machine Vision Technologies to Connect with Humans

The Journal of the Institute of Electrical Engineers of Japan ◽

10.1541/ieejjournal.125.692 ◽

2005 ◽

Vol 125 (11) ◽

pp. 692-695

Author(s):

Kazunori UMEDA ◽

Yoshimitsu AOKI

Keyword(s):

Machine Vision

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Multifunctional stereoscopic machine vision system with multiple 3D outputs

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.2.sda-142 ◽

2020 ◽

Keyword(s):

Machine Vision ◽

International Symposium ◽

Fast Track ◽

Vision System ◽

Machine Vision System ◽

Stereoscopic Displays ◽

Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.

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A Flying Gray Ball Multi-illuminant Image Dataset for Color Research

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.5.050411 ◽

2020 ◽

Vol 64 (5) ◽

pp. 50411-1-50411-8

Author(s):

Hoda Aghaei ◽

Brian Funt

Keyword(s):

Machine Vision ◽

Spectral Reflectance ◽

Color Constancy ◽

Ground Truth ◽

Practical Approach ◽

Post Processing ◽

Illumination Estimation ◽

Processing Step ◽

Image Dataset

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

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Effect of Field Setup on Accuracy of Traffic Data Collection Using Machine Vision Technology

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1804-09 ◽

2002 ◽

Vol 1804 (1) ◽

pp. 62-66 ◽

Cited By ~ 1

Author(s):

Sunita Nadella ◽

Lloyd A. Herman

Keyword(s):

Machine Vision ◽

Focal Length ◽

Reference Points ◽

Specific Reference ◽

Traffic Data ◽

Video Traffic ◽

Vertical Angle ◽

Traffic Data Collection ◽

Camera Position ◽

Parameter Ranges

Video traffic data were collected in 24 combinations of four different camera position parameters. A machine vision processor was used to detect vehicle speeds and volumes from the videotapes. The machine vision results were then compared with the actual vehicle volumes and speeds to give the percentage errors in each case. The results of the study provide a procedure with which to establish camera position parameters with specific reference points to help machine vision users select suitable camera positions and develop appropriate measurement error expectations. The camera position parameters that were most likely to produce the least overall volume and speed errors, for the specific site and field setup with the parameter ranges used in this study, were the low height of approximately 7.6 m (25 ft), with an upstream orientation (traffic moving toward the camera), a 50-mm (midangle) focal length, and a 15° vertical angle.

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