## 1 Introduction
[19] proposed a new object detection framework classification priority network (CPN) and a new classification network multi-group convolutional neural network (MG-CNN) to detect steel surface defects, using the You Only Look Once: Unified, Real-Time Object Detection (YOLO) neural network--the accuracy rate of hot-rolled strip surface defects could reach more than 94% and the classification rate is above 96%; [PERSON], et al. [20] proposed periodic surface defect detection of steel plates based on deep learning to improve the detection rate by improving the Long Short-Term Memory (LSTM) network; and [PERSON], et al. [21] proposed a DCNN (deep convolutional neural network) to detect the micro-defects of metal screw end faces, and the detection accuracy could reach 98%. The image detection obtained by directly taking photos of such a plane is mature and the detection accuracy is generally good. For the image acquisition and detection of irregular surfaces, there is no unified method. Different methods are adopted according to different objects. [PERSON], et al. [22] proposed using vehicle ground-penetrating radar to obtain irregular railway subgrade images. The improved Fast R-CNN was used to identify hazards and compared with traditional neural network methods; [PERSON], et al. [23] used a 3D laser to acquire the defect image of axial, and then conducted deep learning to achieve the high-precision and rapid detection of lateral defects such as fracture, scour and abrasion on railway surfaces; [PERSON], et al. [24] directly adopted fixed-photographing methods for surface irregularities of  
Figure 1: Common metal shaft surface defects, (**a**) pits, (**b**) breach, (**c**) abrasion, (**d**) scratch. automobile hubs and achieved the identification of automobile hub surface defects based on the improved Faster R-CNN, comparing this with the current state-of-the-art YOLOv3. The image acquisition of regular curved surfaces mainly refers to the image acquisition of a rotating curved surface, which is often performed by multiple cameras, and then multiple photos are synthesized. For example, [PERSON] et al. [25] proposed taking multiple photographs of the cylindrical surface, then synthesizing these photographs, finally obtaining the complete defect picture. However, with the advent of line-scan cameras and linear light sources, line-scanning is generally used to acquire images for rotating curved surfaces. For example, [PERSON], et al. [26] used line-scanning to obtain images for the circular curved surfaces of chemical fiber paper tubes. The defect image was detected using Faster R-CNN, and the accuracy rate was 98%. [PERSON] et al. [27] proposed using a line scan camera for image acquisition on the surface of a cylindrical work piece and using Faster R-CNN to detect defects. There is currently no mention of how to obtain high-quality surface images in the case of a highlighted surface. There are many methods to detect defects after obtaining an image with defects. There are methods based on shallow neural networks. For example, [PERSON], et al. [28] proposed the use of cascaded autoencoder structures for the segmentation and localization of defects, and by using shallow convolutional neural networks, metalsurface defects are automatically detected and identified. Furthermore, there are deep learning methods based on traditional deep neural networks used to directly identify defects. For example, [PERSON], et al. [29] used traditional deep learning methods to detect defects on the surface of products; that is, the traditional learning method is directly adopted, in which the image is segmented first, then the segmented image is to added into deep learning, and then a group of deep learners are used and the three deep learning methods are compared for the detection effects.