Guo Bo, Li Xu
Nanchang Key Laboratory of Welding Robot & Intelligent Technology, Nanchang Institute of Technology, Nanchang, 330099, China.
Sci Rep. 2024 Sep 30;14(1):22628. doi: 10.1038/s41598-024-73516-3.
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.
电弧气泡的稳定性是水下湿法焊接过程的一个关键指标。然而，在这种环境下检测电弧气泡边缘的研究有限，传统算法往往产生模糊和不连续的结果。为应对这些挑战，我们提出一种基于深度迁移学习的新型电弧气泡边缘检测方法，用于处理水下湿法焊接图像。所提出的方法集成了两个训练阶段：预训练和微调。在预训练阶段，使用一个大的源域数据集来训练VGG16作为特征提取器。在微调阶段，我们引入了注意力-尺度-语义（ASS）模型，它由一个卷积块注意力模块（CBAM）、一个尺度融合模块（SCM）和一个语义融合模块（SEM）组成。ASS模型在特定于水下湿法焊接的小目标域数据集上进一步训练，以微调模型参数。CBAM可以自适应地对特征图加权，专注于更关键的特征以更好地捕捉边缘信息。SCM训练方法通过组合多尺度特征最大化特征利用率并简化训练。此外，SEM的跳跃结构有效地减轻了高层网络中的语义损失，提高了边缘检测的准确性。在BSDS500数据集和一个自建的水下湿法焊接数据集上，将ASS模型与传统边缘检测模型——更丰富的卷积特征（RCF）、全卷积网络（FCN）和UNet——以及最先进的模型LDC和TEED进行了评估比较。在平均绝对误差（MAE）、准确率和其他评估指标方面，ASS模型始终优于这些模型，证明了其在检测水下湿法焊接图像中的电弧气泡边缘时具有有效且稳定的边缘检测能力。
