# A Lightweight Residual Model for Corrosion Segmentation with Local Contextual Information  
Jingxu Huang  
School of Automation, Beijing Information Science and Technology University (BISTU), Beijing 100192, China  
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[PERSON]  
Correspondence: [EMAIL_ADDRESS]  
Lang Xiang  
School of Automation, Beijing Information Science and Technology University (BISTU), Beijing 100192, China  
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Guangrui Li  
School of Automation, Beijing Information Science and Technology University (BISTU), Beijing 100192, China  
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Yiqing Zhang  
School of Automation, Beijing Information Science and Technology University (BISTU), Beijing 100192, China  
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Wenbai Chen  
School of Automation, Beijing Information Science and Technology University (BISTU), Beijing 100192, China  
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Footnote 2: email: {jangxu.chenxu  
networks (FCNs) for the most part [8]. However, the traditional FCN [8] framework follows the idea of classification and ignores contextual features. Some central moment (CM) [9; 10; 11] models (CM-FCN) capture the long-range dependencies of semantic features well by performing a contextual module after the encoding stage but overlooking the detail dependencies among pixels. Based on the above analysis, a new, light, deep-learning model is proposed in this paper that captures contextual information at all stages for corrosion segmentation as shown in Figure 1. The novelty of this study and its superiority over other studies comes from three aspects:  
1. We present a mixture of large and small kernels to acquire spatial and semantic contextual information and perform superior corrosion segmentation.
2. We follow the ShuffleNetv2 to alleviate the computational overhead caused by large kernels and to embed high-precision models into mobile devices more appropriately.
3. The creation of a fused multi-scale feature promotes information acquisition under limited resources.  
To summarize, our main contributions are  
* The design of large and small convolution kernels at all stages in the encoder to capture the long-distance dependencies between pixels and local detailed information and the design of a novel, simple decoder structure to fuse multi-scale information to improve model accuracy in an end-to-end residual deep-learning framework.
* The reduction of the model size by borrowing the core idea of ShuffleNetv2. The depthwise separable convolution and channel split operations reduce the computational overhead from large kernels, and the channel shuffle further improves feature representation capability.
* The design of two differently sized models to accommodate a variety of application scenarios. Extensive experimental results on a benchmark dataset showed that both models outperformed the state-of-the-art methods in model size and in accuracy trade-off for corrosion segmentation and degree evaluation.  
Figure 1: Common architectures for semantic segmentation. CM: contextual modules, CF: contextual features. (**a**) The FCN framework follows the guidelines of classification, which ignore contextual information. (**b**) CM–FCN model uses CM to extract contextual features of semantic information only in the last stage of the network. (**c**) The proposed model extracts the contextual features at all stages of the network.