# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 2 Related Works  
Based on differences in features, traditional machine vision-based defect detection methods can be mainly classified into three categories: texture-based, color-based, and shape-based methods [15; 16]. [PERSON] et al. [17] successfully identified wood-grain defects using histogram features, achieving a recognition rate of 99.8%. Additionally, [PERSON] et al. [18] detected defects on ceramic surfaces using the gray-level co-occurrence matrix technique, with an accuracy of 92.31%.  
Today, image segmentation techniques have been overshadowed by the widespread use of deep learning models, which offer improved segmentation results [19]. [PERSON] et al. introduced SegNet [20], an architecture based on fully convolutional networks (FCN), utilizing a novel technique called reverse pooling for non-linear upsampling. [PERSON] et al. enhanced U-Net by incorporating residual blocks [21], facilitating the extraction of additional features at each layer. Due to its superior performance and efficiency in feature extraction, Res-UNet serves as the fundamental model for numerous architectures in deep learning [22]. [PERSON] et al. utilized a slightly modified U-Net architecture, integrating weighted cross-entropy and generalized dice loss into a composite loss function, resulting in a substantial enhancement in segmentation accuracy [23]. However, these methods transmit all extracted features to the decoder stage through skip connections.  
Several detection methods incorporating multi-scale and multi-level feature extraction have been proposed for powerful feature representation [21; 24]. These methods use feature pyramid modules as multi-scale feature extractors, capturing rich contextual information under various resolution conditions. Additionally, [PERSON] et al. [25] employed a densely connected convolutional neural network for crack detection, introducing a connectivity loss function between pixels to overcome crack dispersion in the output of deconvolution layers. [PERSON] et al. proposed CrackNet-V, an efficient deep neural network for 3D asphalt pavement crack detection, building upon the previous work of CrackNet [26; 27]. CrackNet-V uses multiple small convolutional kernels (3 \(\times\) 3) to increase the network depth, improving accuracy and computational efficiency without adding extra parameters. [PERSON] proposed an adaptive deep fusion capsule network (ADFCNet) [28; 29], which involves the deep multimodal fusion of high-level features and employs a capsule classifier for roughness recognition. This approach utilizes surface images of artifacts to detect roughness levels.  
Traditional image classification models often struggle to precisely locate defects, while object detection models require a large number of prior boxes to cope with defects that vary in shape and size, which can lead to inaccurate predictions in cases of small sample sizes. Moreover, although semantic segmentation models can visualize defects, they are unable to automatically decide on over-detection or under-detection. Therefore, this research proposes a new approach that combines a segmentation network with a decision network to detect defects. Specifically, this paper uses the segmentation network to accurately locate defects and the decision network to make decisions about over-detection or under-detection in the detection results.  
The deep learning techniques discussed above are applied to defect segmentation and localization in surface defect detection. However, for the accurate segmentation and localization of defect areas, it is crucial to obtain a qualitative result indicating the presence of defects. Therefore, this paper proposes a novel DEU-Net that integrates segmentation and classification. Improvements are made to the U-Net in the segmentation part, and the model is applied to surface defect detection.