Learning Instance Segmentation of Civil Engineering Scene Based on Graph Attention Mechanism and Super-Resolution Network Model

Wanli Gu; Zongjie Hu

doi:10.16670/j.cnki.cn11-5823/tu.2023.02.16

Citation: Wanli Gu, Zongjie Hu. Learning Instance Segmentation of Civil Engineering Scene Based on Graph Attention Mechanism and Super-Resolution Network Model. Journal of Information Technologyin Civil Engineering and Architecture, 2023, 15(2): 87-91. doi: 10.16670/j.cnki.cn11-5823/tu.2023.02.16

2023, 15(2): 87-91. doi: 10.16670/j.cnki.cn11-5823/tu.2023.02.16

Learning Instance Segmentation of Civil Engineering Scene Based on Graph Attention Mechanism and Super-Resolution Network Model

Wanli Gu , Zongjie Hu

State Grid Shanghai Municipal Electric Power Company, Shanghai 200120, China

Web Publishing Date: 2023-04-20

There are a number of problems in the intelligent recognition of substation civil construction site, for instance, complexity of construction scene, difficulty of target recognition and segmentation, and asymmetry of information, etc. To solve the problems above, this paper proposes an intelligent scene recognition and analysis technology based on the graph attention mechanism and super-resolution network model, which uses the graph volume spirit network and attention mechanism network to extract the deep-seated features of target images. Through the pixel super-resolution technology combined with bilinear interpolation and deconvolution, it is feasible to obtain clear the object boundary in images and realize the instance segmentation of civil engineering scene. The results in the paper show that the graph attention mechanism and super-resolution network model effectively solves the problems of insufficient target boundary information and poor case segmentation accuracy in aerial images. What's more, it can accurately segment the target in substation scene, and the edge boundary of target is clear.

Key words: Substation, Construction Site, Graph Attention Mechanism, Instance Segmentation, Super-Resolution

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