# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 2 Related Work  
The traditional methods are as follows. Traditional methods detect defective objects by making use of handmade features (each pixel is classified as a defect or non-defect) to evaluate the saliency value [15; 16]. For example, a novel probabilistic salience framework was proposed by [17] to utilize two specific saliency features to represent the initial significance of each pixel, and this changed the intensity of each pixel according to significance during the iterative process. [PERSON] et al. [18] took a saliency detection problem as the task of multi-instance learning (MIL), where the super-pixels evaluated by the proposed proposals are instances of MIL. Their work improved the accuracy of extracting significant targets at the expense of computational cost. A novel model with two structural regularization methods was constructed by [PERSON] et al. [19], which suppose that images can be compressed into two matrices: the low-level matrix and the low-level matrix, where the former matrix represents the visual consistent background and the latter represents the different foreground object regions. The disadvantage of traditional salient object detection is that artificial features are easy to miss details due to the influence of noise and clutter. Therefore, if the image is unevenly illuminated or the contrast between the defective and non-defective areas is low, these hand-crafted features will limit their application due to the difficulty of obtaining satisfactory results. Patch-wise deep methods are discussed as follows. Inspired by the superior performance of the image classification of deep convolutional neural networks, patch-wise deep salient object detection methods classify patches as salient or non-salient objects from local image pixels which are extracted from a single or multiple scales [20]. Many patch-wise deep methods which have been proposed, such as by [PERSON] et al. [21], tackle low-level saliency cues or priors and do not produce good enough saliency detection results by proposing a multi-context deep learning framework, which employ deep convolutional neural networks to model saliency of objects in images. For learning high-quality visual saliency objects, [PERSON] et al. [22] proposed a patch-wise deep salient object detection method which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. Spatial information is missing due to the introduction of the fully connected layer and, thus, these methods usually output coarse saliency maps. The FCN-based methods are discussed as follows. Due to the powerful representation ability of the fully convolutional neural network, the salient object detection methods based on the fully convolutional neural network has been significantly improved compared to the depth method of the patch. The methods based on fully convolutional neural networks can extract multi-level features. The low-level features from the shallow layer are used by fully convolutional neural network to reconstruct the spatial details, and the high-level features codes from the deep layer are used to obtain the semantic information of the abstract description of the object. In [23], two sub-models based on pooling operations were used to gradually optimize the extracted features and generate the well-structured saliency maps. [PERSON] et al. [24] proposed a novel model focusing on both context features and spatial features, and this model obtains saliency images with rich boundary details by fusing the channel attention and the spatial feature mapping spatial attention of context feature mapping. [PERSON] et al. [25] proposed a novel cascading framework in which the decoder discards shallow, unimportant features to accelerate the model and directly refine the saliency map obtained by deep feature fusion. The framework enables fast and accurate object detection. A detailed and comprehensive survey of deep saliency detection can be seen in the literature [12]. Coarse-to-fine deep methods are as follows.