WPSeg-Net: A Boundary-Guided Dual-Branch Network for Molten Pool Segmentation
TL;DR
An efficient boundary-guided dual-branch network, WPSeg-Net, is proposed, which employs ResNet-34 as the encoder, integrates a lightweight Transformer module for global context modeling, and adopts BiFPN for multi-scale feature fusion.
Abstract
The geometric morphology and dynamic evolution of the molten pool are key indicators of heat input, metal transfer, and solidification behavior during welding, making them critical for quality monitoring and process control. To address the challenges of molten pool image segmentation under complex conditions, including strong arc interference, blurred boundaries, small foreground regions, and significant shape variations, a MIG welding-based visual acquisition system is established to construct a multi-condition dataset. Based on this dataset, an efficient boundary-guided dual-branch network, WPSeg-Net, is proposed. The network employs ResNet-34 as the encoder, integrates a lightweight Transformer module for global context modeling, and adopts BiFPN for multi-scale feature fusion. A region branch and a boundary branch with a mutual guidance mechanism are further designed to improve segmentation accuracy and boundary refinement. Experimental results demonstrate that WPSeg-Net achieves an IoU of 92.83% and a Dice score of 96.28%, improving by 0.98% and 0.58% over DeepLabV3+, respectively. Meanwhile, the proposed method reduces the parameter size from 22.43 MB to 21.78 MB and decreases FLOPs from 62.25 G to 31.90 G, achieving a better balance between segmentation performance and computational efficiency.