We propose an industrial measurement and inspection system for steel workpieces eroded by electrical discharge machining, which uses deep neural networks for surface roughness estimation and defect detection. Specifically, a convolutional neural network (CNN) is used as a regressor in order to obtain steel surface roughness and a CNN based on spatial pooling pyramid is applied for defect classification. In addition, a new method for the region of interest selection based on morphological reconstruction and mean shift filtering is proposed for defect detection and localization. The regressor and classifier based on deep neural networks proposed here outperform state-of-the-art methods using handcrafted feature extraction. We achieve a mean absolute percentage error of 7.32% on roughness estimation; on defect detection, our approach yields an accuracy of 97.26% and an area under the ROC curve metric of 99.09%.

我们提出了一种用于电火花加工腐蚀的钢工件的工业测量和检查系统，该系统使用深度神经网络进行表面粗糙度估计和缺陷检测。具体而言，使用卷积神经网络（CNN）作为回归变量以获得钢表面粗糙度，并将基于空间池金字塔的CNN用于缺陷分类。此外，提出了一种基于形态重构和均值漂移滤波的感兴趣区域选择新方法，用于缺陷检测和定位。本文提出的基于深度神经网络的回归器和分类器的性能优于使用手工特征提取的最新方法。在粗糙度估计上，我们实现了7.32％的平均绝对百分比误差；在缺陷检测上，