| MECHANICAL ENGINEERING |
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Detection algorithms and optimization of image processing for visual sensors using combined laser structured light |
| SUN Bowen, ZHU Zhiming, GUO Jichang, ZHANG Tianyi |
| Key Laboratory for Advanced Materials Processing Technology of Ministry of Education, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China |
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Abstract Visual sensors based on laser structured light are widely used in the welding field for detection of welding grooves and tracking of weld seams. This paper describes a visual sensor based on combined laser structured light where the light paths within the visual sensor are designed to eliminate the need to calibrate the external sensor parameters for different positions and postures to provide on-line detection of welding groove parameters using only the internal sensor parameters (which require calibration) and the feature point coordinates of the welding groove image. Thus, this method is much more adaptable than previous methods. The image processing is optimized by improving and combining various image processing algorithms. A dynamic region of interest region extraction algorithm is used to quickly find the region that includes the laser lines and feature points to significantly improve the subsequent image processing speed. Then, a top hat transformation and self-adaptive binarization are used to homogenize the gray values of the laser lines that differentiates the laser lines from the background. After that, an edge recognition algorithm based on the Laplacian of Gaussian (LOG) operator is used to extract the single pixel edges of the laser lines which are used to then identify the discrete points of the laser center lines that are fit to linear equations by the least squares method. Finally, these linear equations are used to accurately calculate the image coordinates of the feature points of the welding groove. All these image processing algorithms were implemented in Visual Studio for tests to detect V-shaped welding grooves with various characteristic parameters. The detection errors are all within ±4%, which shows that the detection algorithms are reasonable and applicable for image processing of welding processes with visual sensors.
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| Keywords
visual sensor
image processing
dynamic region of interest extraction
top hat transformation
adaptive binarization
edge recognition
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Issue Date: 01 June 2019
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2019,
Vol. 59
