Welding pool image segmentation method based on lightweight DeepLabV3+ network

To accurately and quickly extract the molten pool image in the welding process, a molten pool image segmentation method based on a lightweight DeepLabV3 + network was proposed. The backbone network of Deep-LabV3+ was replaced by the optimized MobileNetV2 network from Xception to reduce the number of model Parameters. The Coordinate Attention (CA) mechanism was introduced to improve the model's ability to extract the molten pool image. The training method of transfer learning was used to solve the scarcity of molten pool samples and improve the accuracy and generalization ability of the model. The experimental results showed that the Mean Inter-section over Union (MloU)of the improved model under the molten pool data set was 94. 65 %, the Mean Pixel Accuracy (MPA) was 96. 67 %, the inference time of a single picture was 11. 09 ms, and the model parameter quantity was 5. 81 M. Compared with classical networks such as SegNet, PSPNet, UNet and DeepLabV3 +, the improved algorithm had a smaller number of model parameters, shorter single-image inference time and maintains a higher mean intersection over union, which could better balance the image segmentation accuracy and real-time Performance. © 2025 CIMS. All rights reserved.