Arc bubble edge detection method based on deep transfer learning in underwater wet welding

Abstract

The stability of arc bubble is a crucial indicator of underwater wet welding process. However , limited research exists on detecting arc bubble edges in such environments , and traditional algorithms often produce blurry and discontinuous results. To address these challenges , we propose a novel arc bubble edge detection method based on deep transfer learning for processing underwater wet welding images. The proposed method integrates two training stages: pre-training and fine-tuning. In the pre-training stage , a large source domain dataset is used to train VGG16 as a feature extractor. In the fine-tuning stage , we introduce the Attention-Scale-Semantics (ASS) model , which consists of a Convolutional Block Attention Module (CBAM) , a Scale Fusion Module (SCM) and a Semantic Fusion Module (SEM). The ASS model is further trained on a small target domain dataset specific to underwater wet welding to fine-tune the model parameters. The CBAM can adaptively weight the feature maps , focusing on more crucial features to better capture edge information. The SCM training method maximizes feature utilization and simplifies training by combining multi-scale features. Additionally , the skip structure of SEM effectively mitigates semantic loss in the high-level network , enhancing the accuracy of edge detection. On the BSDS500 dataset and a self-constructed underwater wet welding dataset , the ASS model was evaluated against conventional edge detection models—Richer Convolutional Features (RCF) , Fully Convolutional Network (FCN) , and UNet—as well as state-of-the-art models LDC and TEED. In terms of Mean Absolute Error (MAE) , accuracy , and other evaluation metrics , the ASS model consistently outperforms these models , demonstrating edge detection capabilities that are both effective and stable in detecting arc bubble edges in underwater wet welding images.