Alonso Marcos, Maestro Daniel, Izaguirre Alberto, Andonegui Imanol, Graña Manuel
Robotics and Automation Group, Electronic and Computer Science Department, Faculty of Engineering, Mondragon University, Loramendi Kalea, 4, 20500 Arrasate-Mondragon, Spain.
Computational Intelligence Group, CCIA Department, UPV/EHU, Paseo Manuel de Lardizabal 1, 20018 San Sebastian, Spain.
Sensors (Basel). 2021 Oct 23;21(21):7024. doi: 10.3390/s21217024.
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.
表面平整度评估对于通过辊式矫直和切割由钢卷制成的金属板的质量控制至关重要。基于机械接触的平整度传感器正被现代基于激光的光学传感器所取代，这些光学传感器能够为平整度指数计算提供金属板表面的精确且密集的重建。然而，由于脱脂、清洁、抛光、剪切和输送辊系统等机械过程引起的振动，这些光学传感器捕获的表面距离图像会被非常特殊的噪声所破坏。因此，高质量的平整度光学测量系统在很大程度上依赖于用于提取真实表面高度图像的图像去噪方法的质量。本文提出了一种深度学习架构，用于从安装在轧制和剪切生产线中的基于激光的距离传感器获得的距离图像中去除这些特殊类型的噪声，以便能够从清晰的距离图像中进行准确的平整度测量。所提出的卷积盲残差去噪网络（CBRDNet）由一个噪声估计模块和一个通过对语义卷积神经网络进行特定改编实现的噪声去除模块组成。CBRDNet在合成和真实噪声距离图像数据上均得到了验证，这些数据展示了在整个金属板生产过程中出现的最关键类型的噪声。真实数据是从安装在辊式矫直和定尺切割生产线中的单个激光线三角测量平整度传感器获得的。对合成数据集和真实数据集的计算实验清楚地表明，与传统的一维和二维滤波方法以及基于卷积神经网络的最新去噪技术相比，CBRDNet具有卓越的性能。实验验证结果表明，相对于基于传统一维和二维滤波方法的解决方案，误差降低了高达15%，相对于最近文献中报道的其他深度学习去噪架构，误差降低了10%至3%。
