## 1 Introduction
According to the instructions of ISO 4288, the workpiece shall be properly positioned so that the direction of the section corresponds to the maximum value of the height of the roughness parameters (arithmetic height parameter _Ra_, 10-point roughness _Rz_). This direction will be normal to the lay of the surface being measured. Generally, this direction can be assessed by visual examination. Therefore, some measure errors occur. Figure 2 illustrates the influence of the surface roughness measuring direction, where the red plane indicates the idle measure plane of the measured surface and the  
Figure 1: Profilometer sketch. yellow and green planes indicate the measure planes when the incline is 8\({}^{\circ}\) and 4\({}^{\circ}\), respectively. The measuring profiles (Figure 2a-c) show that measuring direction errors lead to frequency changes compared with the idle direction. Figure 2d,e also show the measured profile errors. These errors may result in roughness measuring errors. There are many different roughness parameters in use, but Ra is by far the most common, though this is often for historical reasons and not for particular merit. National standard (GB/T 1031-2009) also recommends \(Ra\) as the preferred roughness parameter. Meanwhile, in the given surface roughness specification of the agricultural machinery manufacturing company, Ra is the only required surface roughness constraint. Therefore, \(Ra\) is chosen as the representative of surface roughness in this research. Hough transform, projection characteristics, Fourier transform and shearlet transform are algorithms commonly used for skew correction [14; 15; 16; 17]. However, these algorithms are mainly proposed for detecting deviations of the document orientation angles from the horizontal direction or the vertical direction. In this research, the investigated subjects are textures of the machined surface. Prior researchers have shown that acquired surface images contain strong noise in a broadband frequency range [18]. Therefore, in order to ensure more meaningful surface roughness estimation results, some proper preprocessing of skew correction is indispensable. Recently, the neural network has aroused heated discussion in the scientific and industrial communities because it achieves good performance for pattern recognition [19; 20; 21; 22; 23; 24]. The concept of pattern recognition can be defined as identifying or classifying complex signal samples or objects [25]. Surface roughness evaluation can also be considered a special case of pattern recognition. Although it is finding more and more applications in pattern recognition problems, the neural network of the deeper layers also involves more sophisticated training process. Aiming at handling this problem, residual network (ResNet) has recently been proposed for optimizing the deep network and has achieved huge success [26]. [PERSON] investigated the change of classification accuracy when the acquired images are preprocessed by transformation-based methods [27]. Results indicate that the classification accuracy of the convolutional neural network is substantially affected by global translation, rotation, and scaling. As such, variations in measurement direction influence not only the roughness measurement results but also the classification accuracy. Inspired by the idea of ResNet, we present a surface roughness evaluation method using complex wavelet-enhanced ResNet. This method is performed on the adjusted images processed by the improved texture skew correction algorithm. In this method, 2D-DTCWT with fixed decomposition depth is performed to retrieve the main signal component within the adjusted images. ResNet is utilized to ensure the automatic classification of the surface roughness.