# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 1 Introduction  
Strip steel has been widely used in aerospace, shipbuilding, automotive, and other fields, and minor quality defects may adversely affect the performance and service life of strip steel, resulting in huge economic losses [1]. Therefore, it is a crucial task to detect the defects in the strip steel to guarantee the quality of industrial production. However, this task is often handled manually, which is laborious and time-consuming. Thus, many automatic surface defection methods based on computer vision technologies are used to detect defect objects in the strip steel. Inspired by the human visual system, saliency detection [2] has been used to detect defective objects by filtering out plenty of redundant background interferences. Additionally, saliency detection has been widely to enable image understanding [3], person reidentification [4; 5], defect detection [6], semantic segmentation [7], and so on. In industrial surface defect detection, the saliency object detection method is mainly composed of methods based on traditional models or based on deep learning. The former uses manually designed feature extraction factors to extract visual features, such as Gabor filters [8] and wavelet transform [9], and then a classification is applied to the extracted features to identify the corresponding defect objects [10]. However, the model is very sensitive to changes in real-world situations and susceptible to light and cluttered backgrounds. Recently, deep learning has been introduced into surface defect detection due to its excellent ability to automatically learn deep features of images, which greatly solves the problems existing in the ones based on traditional methods. [PERSON] and [PERSON] [11]applied the convolutional neural networks to steel surface defect detection and improved the network recognition performance using the normalized method. Benefiting from strong representation ability, saliency detection methods based on deep learning, especially those based on convolutional neural networks, have achieved remarkable results [12]. However, the problems still exist in terms of target integrity and boundary conservation. From Figure 1a, the traditional methods cannot detect defective targets with slender features due to artificial features failing to effectively capture the global and high-level semantic information of the defective object. In addition, the existing deep learning methods based on saliency defect detection still have deficiencies in capturing the complete boundary of defective objects (as shown in Figure 1b). Last but not least, it is difficult for saliency defect detection methods to segment small defect objects from the compact background images (see Figure 1c). In addition, from Figure 1, we can find that inclusions account for a relatively small proportion of the defective image of strip steel; patches often have dark and uneven illumination features; scratches have a big difference in size and shape. Therefore, accurately detecting surface defects of strip steel is a challenging task. Under this condition, early models fail to obtain enough features and, thus, they often detect incomplete defect regions and mistakenly confuse the background region with the defect region. In order to solve the problem above, we propose a nested U-shaped residual codec network (NURCNet) for strip steel defect detection, which consists of three submodules. The first submodule is the encoder network, which consisting of the attention mechanism and the fully convolutional residual network; the fully convolutional network is used to extract rich multiscale features and the lightweight attention mechanism is to make the extracted features pay more attention to defective objects. Between the encoder and  
Figure 1: Three common strip surface defects, as follows: (**a**–**c**) represent ordinary, close to the defects of the edge, and the chaotic background defective image, respectively. The red box indicates the defective region.