## 1 Introduction  
An arc magnet [1] generally refers to a curved, magnetic object made of ferrite, NdFeB, AlNiCo, etc. It is mounted on the stator or rotor of the motor and is essential to generate a constant magnetic field in a permanent magnet motor. Surface defects, such as cracks, blowholes, frag, break, unevenness, and blot, may appear on an arc magnet due to the complex manufacturing process. These defects tend to seriously affect the mechanical strength and magnetic properties of an arc magnet, leading to abnormal motor operation and even safety accidents [2]. The current widely used surface defect detection method for arc magnets still relies on manual observation methods under visible light. However, such methods require extensive manual experience and are characterized by vague detection criteria, unstable recognition accuracy, low execution efficiency, and weak automation [3]. In order to ensure the large-scale production of diverse high-quality arc magnets, it is particularly urgent and important to develop accurate, fast, and automated identification methods for arc magnet surface defects. Due to the visibility of surface defects, machine vision [4] is widely recognized as a promising technology for identifying surface defects on arc magnets. Over the last decade, several studies [5; 6; 7] have sought to determine typical surface defects by analyzing the surface images of arc magnets. In general, the image-based surface-defect identification of arc magnets can be defined by two approaches: traditional mathematical methods [8] and deep-learning methods [9]. The former focuses on presenting information rules through the discrete transformation of images to discover and extract defect features. For example, [PERSON] et al. [10] combined non-sampling outline transformation and texture characteristics to detect surface defects from arc magnet images; the defect extraction accuracy by this method reached 93.57%. [PERSON] et al. [11] proposed an image recognition algorithm for arc magnet surface defects based on a new anisotropic diffusion filtering model that performed well in the edge extraction of surface defects, resulting in a significant improvement in defect recognition accuracy. [PERSON] et al. [12] proposed a crack defect detection algorithm based on the Contourlet transform and singular value decomposition to build relationships between image grey features and arc magnet surface defects, effectively overcoming noise interference in identifying the crack and fray. Although these methods are conducive to extracting the image features that represent surface defects, they are extremely sensitive to the matching and selecting of discrete laws and feature distributions, which is unsuitable for complex and weak surface defects with features of poor regularity. On the other hand, with the advancement of recent developments in artificial intelligence, deep learning [13] has made increasingly noteworthy achievements in solving the identification problem of arc magnet surface defects. A growing number of novel learning models are being created to autonomously learn and identify surface defect information in arc magnet images. For instance, [PERSON] et al. [14] presented a segmentation method of the weighted You Only Look At CoefficientTs (YOLACT) model to solve the problems of slow speed and low segmentation accuracy of different defects on the magnetic tile surface, achieving good segmentation results at the segmentation speed of 24.40 fps and mean average precision of 53.44. [PERSON] et al. [15] used UPM-DenseNet to design an online two-stage model for arc magnet surface defects, improving the accuracy and speed of the recognition regarding weak defects. [PERSON] et al. [16] provided a semi-supervised learning method based on pseudo-labeling to address the time-consuming and error-prone problems of surface defect classification of magnetic tiles with limited labeled samples. [PERSON] et al.