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In the manufacturing process of heavy industrial equipment, the specific unitin the welding diagram is first manually redrawn and then the correspondingsheet metal parts are cut, which is inefficient. To this end, this paperproposes a U-net-based method for the segmentation and extraction of specificunits in welding engineering drawings. This method enables the cutting deviceto automatically segment specific graphic units according to visual informationand automatically cut out sheet metal parts of corresponding shapes accordingto the segmentation results. This process is more efficient than traditionalhuman-assisted cutting. Two weaknesses in the U-net network will lead to adecrease in segmentation performance: first, the focus on global semanticfeature information is weak, and second, there is a large dimensionaldifference between shallow encoder features and deep decoder features. Based onthe CBAM (Convolutional Block Attention Module) attention mechanism, this paperproposes a U-net jump structure model with an attention mechanism to improvethe network's global semantic feature extraction ability. In addition, a U-netattention mechanism model with dual pooling convolution fusion is designed, thedeep encoder's maximum pooling + convolution features and the shallow encoder'saverage pooling + convolution features are fused vertically to reduce thedimension difference between the shallow encoder and deep decoder. Thedual-pool convolutional attention jump structure replaces the traditional U-netjump structure, which can effectively improve the specific unit segmentationperformance of the welding engineering drawing. Using vgg16 as the backbonenetwork, experiments have verified that the IoU, mAP, and Accu of our model inthe welding engineering drawing dataset segmentation task are 84.72%, 86.84%,and 99.42%, respectively.

Segmentation method of U-net sheet metal engineering drawing based on CBAM attention mechanism