Micro-Scale Spherical and Cylindrical Surface Modeling via Metaheuristic Algorithms and Micro Laser Line Projection

Rodríguez, José Mendoza

doi:10.3390/a15050145

Cited by 3 publications

(2 citation statements)

References 66 publications

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“…This technique utilizes microlasers to project very fine laser lines, and obtains information about the fine features of the surface by observing the reflection or scattering of the laser lines on the surface to be recognized, so as to infer the structure, texture or defects of the surface. Rodríguez 38 presented the recognition of micrometer scale concave, convex. and planar surfaces by means of Hu moment patterns based on micro-laser line profiles.…”

Section: Related Workmentioning

confidence: 99%

STD-Detector: spatial-to-depth feature-enhanced detection method for the surface defect detection of strip steel

Wang,

Liu,

Yang

et al. 2023

J. Electron. Imag.

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Due to the small size, high density, and background noise associated with strip surface defects, the current object detection model commonly faces limitations in performance. To address this issue, we propose a spatial-to-depth feature-enhanced detection method called STD-Detector. The method consists of two types STD-Conv-A and STD-Conv-B. First, the STD-Conv-A module is used in the backbone feature extraction network to expand the field of perception and enable the model to learn a wider range of background information. Then, the STD-Conv-B module is used for feature fusion networks to improve the expression of output features. In addition, we incorporate the convolutional block attention module to mitigate background interference and enhance the performance of the model. Finally, experimental results on the NEU-DET dataset show that our method achieves a mean average precision of 82.9%, which represents a 3.9% improvement over the baseline. Compared with the state-of-the-art model, our method exhibits greater competitiveness in detecting strip surface defects. Moreover, experimental results on the road surface defect dataset show that our method has good robustness.

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Section: Related Workmentioning

confidence: 99%

STD-Detector: spatial-to-depth feature-enhanced detection method for the surface defect detection of strip steel

Wang,

Liu,

Yang

et al. 2023

J. Electron. Imag.

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“…With the emergence of low-cost experimental components, PMP has been widely applied in biomedical industry, industrial inspection, machine vision, profile modeling, reverse engineering, etc. [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An Improved Calibration Method of Grating Projection Measurement System

Sun¹,

Dai²,

Sun³

et al. 2023

Computers, Materials &Amp; Continua

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In the traditional fringe projection profilometry system, the projector and the camera light center are both spatially virtual points. The spatial position relationships specified in the model are not easy to obtain, leading to inaccurate system parameters and affecting measurement accuracy. This paper proposes a method for solving the system parameters of the fringe projection profilometry system, and the spatial position of the camera and projector can be adjusted in accordance with the obtained calibration parameters. The steps are as follows: First, in accordance with the conversion relationship of the coordinate system in the calibration process, the calculation formula of the vertical distance from the camera light center to the reference plane and the calculation formula of the distance between the projector and the camera light center are given respectively. Secondly, according to the projector calibration principle, the position of the projector light axis perpendicular to the reference plane is gained by comparing the parallel relationship between the reference plane coordinate system and the projector coordinate system's Z-axis. Then, in order to fulfill the position restriction that the line between the projector light center and the camera light center must be parallel to the reference plane, the camera's spatial location is adjusted so that the vertical distance between it and the reference plane tends to that between the projector light center and the reference plane. And finally, the three-dimensional (3D) reconstruction of the target object can be finished using the phase height model's system parameters once the aforementioned position limitations are put into practice. Experimental results demonstrate that the method improves the measurement accuracy, and verifies that it is effective and available in 3D shape measurement.

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Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

Rodríguez

2022

Energies

Self Cite

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Industry 4.0 represents high-level methodologies to make intelligent, autonomous, and self-adaptable manufacturing systems. Additionally, the surface modeling technology has become a great tool in industry 4.0 for representing the surface point cloud. Thus, the micro-scale machining technology requires efficient models to represent micro-scale flat and free-form surfaces. Therefore, it is fundamental to perform surface modeling through artificial intelligence for representing small surfaces. This study addressed multi-objective optimization via genetic algorithms and micro laser line projection to accomplish surface models for representing micro-scale flat and free-form surfaces, where an optical microscope system retrieves micro-scale topography via micro laser line coordinates and the multi-objective optimization constructs the flat and free-form surface models through genetic algorithms and micro-scale topography. The multi-objective optimization determines the surface model parameters through exploration and exploitation, and the solution space is deduced via surface data. The surface model generated through the multi-objective optimization fit accurately to the micro-scale target surface. Thus, the proposed technique enhanced the fitting of micro-scale flat and free-form surface models, which were deduced via gray-level images of an optical microscope. This enhancement was validated by a discussion between the multi-objective optimization via genetic algorithms and the micro-scale surface modeling via optical microscope imaging systems.

show abstract

Micro-Scale Spherical and Cylindrical Surface Modeling via Metaheuristic Algorithms and Micro Laser Line Projection

Cited by 3 publications

References 66 publications

STD-Detector: spatial-to-depth feature-enhanced detection method for the surface defect detection of strip steel

STD-Detector: spatial-to-depth feature-enhanced detection method for the surface defect detection of strip steel

An Improved Calibration Method of Grating Projection Measurement System

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

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