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Article

Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach

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Robotics and Automation Group, Electronic and Computer Science Department, Faculty of Engineering, Mondragon University, Loramendi Kalea, 4, 20500 Arrasate-Mondragon, Spain
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Computational Intelligence Group, CCIA Department, UPV/EHU, Paseo Manuel de Lardizabal 1, 20018 San Sebastian, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Steve Vanlanduit
Sensors 2021, 21(21), 7024; https://doi.org/10.3390/s21217024
Received: 17 September 2021 / Revised: 20 October 2021 / Accepted: 21 October 2021 / Published: 23 October 2021
Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature. View Full-Text
Keywords: laser triangulation; metal sheet flatness measurement; smooth surface reconstruction; depth data denoising; Convolutional Neural Networks; deep learning; residual learning laser triangulation; metal sheet flatness measurement; smooth surface reconstruction; depth data denoising; Convolutional Neural Networks; deep learning; residual learning
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MDPI and ACS Style

Alonso, M.; Maestro, D.; Izaguirre, A.; Andonegui, I.; Graña, M. Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach. Sensors 2021, 21, 7024. https://doi.org/10.3390/s21217024

AMA Style

Alonso M, Maestro D, Izaguirre A, Andonegui I, Graña M. Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach. Sensors. 2021; 21(21):7024. https://doi.org/10.3390/s21217024

Chicago/Turabian Style

Alonso, Marcos, Daniel Maestro, Alberto Izaguirre, Imanol Andonegui, and Manuel Graña. 2021. "Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach" Sensors 21, no. 21: 7024. https://doi.org/10.3390/s21217024

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