# A Lightweight Residual Model for Corrosion Segmentation with Local Contextual Information
## 2 Related Works
### Image Semantic Segmentation  
As we know, large models perform better according to common sense, such as Deeplabv3+ [10] using Xception [25] as the backbone of 208.70 MB, and DenseASPP [26] using ResNet50 [19] as backbone of 103.79 MB. However, these high-accuracy large models are unsuitable for mobile devices. Meanwhile, some real-time semantic segmentation approaches are proposed to solve the trade-off between model volume and accuracy. ENet [27] removes the last layer of pooling indexes so that the final number of output feature maps is equal to the number of categories. ICNet [28] uses a cascade of feature fusion units with a low-resolution image to capture semantic information, and medium-resolution and high-resolution images to capture details. ESPNet [29] is an efficient semantic segmentation network for high-resolution images under limited computational resources. LinkNet [30] adds high-resolution residuals in the decoder to recover lost details. ERFNet [31] uses asymmetric convolution to further reduce the number of parameters. These networks havefewer parameters and seek better real-time performance, but the limited receptive fields restrict them to discover rich contextual information for high precise segmentation.  
To solve the above problems, CGNet [32] proposed that a CG block extract local and surrounding contextual features. ContextNet [33] proposed a two-branch network to extract global contextual information and retain detailed information. EDANet [34] uses dense connected asymmetric convolution to obtain information at different scales. Most of these networks carry out extensive dilation convolution to expand the receptive field and aggregate contextual information. However, discontinuous convolution kernels may lead to raster-like segmentation regions. Furthermore, it was the first time that RePLKNet [35] used very large convolution kernels to capture contextual information.  
To apply a combination of traditional machine-learning methods and deep-learning models to segmentation is another way to establish long-range dependencies. Condition Random Field (CRF) as Recurrent Neural Network (RNN), CRFasRNN [36], combines CNN and CRF to build an end-to-end network. EMANet [37] uses the classical EM algorithm to maintain interclass differences and reduce intraclass differences, effectively reducing the complexity of non-local blocks [9; 38; 39; 40]. Furthermore, DANet [9] proposes dual self-attention to model long-range dependencies. CCNet [38] captures long-range dependencies only in the horizontal and vertical directions, effectively reducing the amount of self-attentive computation. There are also some semantic segmentation studies based on ViT [39; 41; 42]. These networks are effective in obtaining global contextual information, but they introduce a large computational overhead. Based on the above analysis, contextual information is very important for improving segmentation accuracy. Current semantic segmentation methods, which use dilated convolution or a global block to extract contextual information, are unable to balance accuracy and computational overhead for corrosion segmentation. To solve this problem, a new lightweight model has been proposed that uses large convolution kernels inspired by RepLKNet together with ShuffleNetv2 [6] to satisfy the high precision and light-weight requirements simultaneously.