<abstract xmlns="http://www.w3.org/1999/xhtml"><p>High precision and high efficiency measurement is becoming an imperative requirement for a lot of mechanical parts. So in this study, a subpixel-level edge detection algorithm based on the Gaussian integral model is proposed. For this purpose, the step edge normal section line Gaussian integral model of the backlight image is constructed, combined with the point spread function and the single step model. Then gray value of discrete points on the normal section line of pixel edge is calculated by surface interpolation, and the coordinate as well as gray information affected by noise is fitted in accordance with the Gaussian integral model. Therefore, a precise location of a subpixel edge was determined by searching the mean point. Finally, a gear tooth was measured by M&amp;M3525 gear measurement center to verify the proposed algorithm. The theoretical analysis and experimental results show that the local edge fluctuation is reduced effectively by the proposed method in comparison with the existing subpixel edge detection algorithms. The subpixel edge location accuracy and computation speed are improved. And the maximum error of gear tooth profile total deviation is 1.9 μm compared with measurement result with gear measurement center. It indicates that the method has high reliability to meet the requirement of high precision measurement.</p></abstract>

High precision and high efficiency measurement is becoming an imperative requirement for a lot of mechanical parts. So in this study, a subpixel-level edge detection algorithm based on the Gaussian integral model is proposed. For this purpose, the step edge normal section line Gaussian integral model of the backlight image is constructed, combined with the point spread function and the single step model. Then gray value of discrete points on the normal section line of pixel edge is calculated by surface interpolation, and the coordinate as well as gray information affected by noise is fitted in accordance with the Gaussian integral model. Therefore, a precise location of a subpixel edge was determined by searching the mean point. Finally, a gear tooth was measured by M&M3525 gear measurement center to verify the proposed algorithm. The theoretical analysis and experimental results show that the local edge fluctuation is reduced effectively by the proposed method in comparison with the existing subpixel edge detection algorithms. The subpixel edge location accuracy and computation speed are improved. And the maximum error of gear tooth profile total deviation is 1.9 μm compared with measurement result with gear measurement center. It indicates that the method has high reliability to meet the requirement of high precision measurement.

High Precision Edge Detection Algorithm for Mechanical Parts

Nanjing University of Aeronautics and Astronautics

Development Planning Department, China Academy of Launch Vehicle Technology

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"High Precision Edge Detection Algorithm for Mechanical Parts"}

High precision and high efficiency measurement is becoming an imperative requirement for a lot of mechanical parts. So in this study, a subpixel-level edge...

High Precision Edge Detection Algorithm for Mechanical Parts

Pubblicato online: 04 apr 2018

Pagine: 65 - 71

Ricevuto: 11 ago 2017

Accettato: 20 mar 2018

DOI: https://doi.org/10.1515/msr-2018-0010

Parole chiave
Vision measurement, edge detection, subpixel-level, Gaussian integral model, normal section line

© 2018 Zhenyun Duan et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

High Precision Edge Detection Algorithm for Mechanical Parts

Pubblicato online: 04 apr 2018

Pagine: 65 - 71

Ricevuto: 11 ago 2017

Accettato: 20 mar 2018

DOI: https://doi.org/10.1515/msr-2018-0010

Parole chiaveVision measurement, edge detection, subpixel-level, Gaussian integral model, normal section line

© 2018 Zhenyun Duan et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Vision measurement, edge detection, subpixel-level, Gaussian integral model, normal section line