## 1 Introduction  
The process of identifying and inspecting abnormalities or irregularities in industrial products is known as surface defect segmentation. The purpose is to ensure that the product meets certain quality standards and is free from defects that could affect its function, performance, or appearance. Current inspection methods can be performed manually by skilled workers, or using automated inspection systems that utilize various techniques such as image processing, computer vision, and machine learning. These systems need to detect various surface defects such as cracks, scratches, dents, and other surface irregularities and provide accurate, fast, and reliable results, thereby increasing the efficiency and reliability of the production process. Unfortunately, this task remains challenging owing to the following concerns: (1) In actual industrial production, the contrast between defective and non-defective parts is often low due to factors such as the environment and production process. (2) Even the same type of defects can have an irregular surface and vary significantly in terms of its area. Figure 1 shows several examples of distinct types of defects commonly observed in industrial production. Traditional surface defect detection methods mainly include non-automated detection and automatic detection. Non-automated detection mainly adopts the method of manual participation, but the detection standards of this method cannot be unified, and intense eye use can cause fatigue, so this kind of method cannot adapt to the requirements of industrial development. As computer vision improves by leaps and bounds, automatic surface defect detection is gradually being applied in the industrial field. [PERSON] et al. [1] employed a Gaussian mixture model for detecting defects on rail surfaces. Additionally, some other hand-crafted feature-based methods, including metals [2] and steel [3], are also employed in surface defect detection and have yielded favorable outcomes in recent years. However, these traditional methods need to manually extract features from images, and they cannot provide satisfactory generalization performance. Over the past several years, the field of deep learning has witnessed significant progress. The successful application of deep learning methods in diverse domains, including computer vision and multimedia, has been widely recognized and has garnered substantial attention from researchers and practitioners alike. Deep learning methods [4] can extract distinguishing features by constructing convolutional neural networks (CNN) and various nonlinear combinations, leading to a reduction in incompleteness resulting from human-designed features. At present, segmentation methods based on CNNs have been universally employed in surface defect detection by designing different neural networks. The FCN (fully convolutional network) [5] is the most primitive structure that adopts semantic segmentation for network training. It changes the final layer of the model by transforming it into a convolutional layer, enabling the transformation of an arbitrary-sized input into a pixel-level output. Based on FCN, [PERSON] et al. [6] introduced an NB-fully convolutional network, achieving good results in the real-time segmentation of defects. Following these pioneering works, various networks for semantic segmentation have emerged, including UNet [7], SegNet [8], and PSPNet [9], and they exhibit excellent performance on many datasets. In addition, the proposal of networks such as ResNet [10] and DeepLab [11] has also promoted the development of semantic segmentation. For surface defect segmentation, the model needs to effectively use the characteristics of objects to distinguish them from the background, different layers of a CNN model have different sensitivities to objects. Fusion with different features is a crucial measure for improving segmentation performance.