## 1 Introduction  
Common defects of automobile wheel hub include fray, hole, nick, and spot. These defects seriously affect the safety and appearance of automobiles [1]. Thus, ineffective and inaccurate manual detection greatly restricts the production level of the wheel manufacturing industry. At present, the surface defect detection of automobile wheel hubs is manual. Therefore, an intelligent surface defect detection method for automobile wheel hubs must be urgently designed for the wheel manufacturing industry. According to the input image, the intelligent surface defect detection method aims to predict defects and their locations. Extensive research has been conducted on surface defect detection methods based on machine vision. Some methods detect region segment defects from the image based on low-level features. These methods include thresholding segmentation and template matching. [PERSON] et al. [2] designed a surface defect detection method for shaft parts based on thresholding segmentation. The image of the shaft part is segmented into binary images by thresholding segmentation, and then the boundary of defect is extracted according to the aspect ratio and the area. Template matching [3] compares the regions of an image with a template using a sliding window to obtain the matching degree of different regions. The region with a matching degree that is greater than the threshold is regarded as a defect. [PERSON] et al. [4] used the weighted least-squares model to detect button defects. From the images of the training dataset, the model adaptively generates a template image, which does not contain any defect, and then obtains the segmentation result by comparing the button image with the template image. The advantage of thresholding segmentation and template matching is their accurate detection of simple defects using few training images. However, these methods cannot detect complex defects accurately and are difficult to apply in some defect detection scenarios. Following the trend of deep learning, some surface defect detection methods based on convolutional neural network (CNN) were proposed and exhibit outstanding performance [5; 6]. [PERSON] et al. [7] proposed a general surface defect detection method based on CNN, which was tested on four defect datasets of steel strips, welds, woods, and fan blades. The results show that the methods based on CNN exhibit good performance on different defect detection scenarios. For the surface defect detection task, an image classification method can obtain image-level semantic information; an object detection method can obtain the location and coarse shape information; and a semantic segmentation method can obtain detailed pixel-level semantic information of the surface defect. Currently, surface defect detection methods based on CNN mainly include object detection and semantic segmentation. [PERSON] et al. [8] used Faster-RCNN with ResNet-101 [9] as the object detection algorithm to detect the scratches and points on the wheel hub in an image with a complicated background. [PERSON] et al. [10] used Focal Loss function to improve the classification loss of Region Proposal Networks module to solve the dataset category imbalance problem. [PERSON] et al. [11] proposed a solution that combines two machine learning based approaches, convolutional autoencoders, and one class support vector machines, so that when analyzing defect image training, samples are labeled for support vector machines in an automatic way and it does not require the intervention. This solution offers very good performance in unknown defect detection, real time performance, and processing of different textures automatically. Compared with image classification and object detection, semantic segmentation can realize defect classification, location, and segmentation. The semantic segmentation network classifies and locates each pixel in an image, so as to accurately locate the object of interest rather than the rough boundary box [12].