焊接缺陷识别中的多模态注意力方法

Multi-modal attention method in welding defect recognition

提出了一种多模态焊接缺陷识别方法,构建了包含3个分支的卷积神经网络,以分别对焊接熔池图片、电弧声、焊接电流和电弧电压进行处理。并在图像分支网络中加入了通道注意力模块和空间注意力模块,以聚焦焊接熔池图片的重要区域。为了验证文中模型的稳定性和可靠性,在自构建的包含10种焊接缺陷的数据集上进行了试验。试验结果表明,双通道注意力机制嵌入到卷积神经网络的浅层效果优于深层。同时,相比于不加注意力机制,双通道注意力机制识别结果的F值得到了明显的提升,为焊接实时分类识别提供参考,有助于焊接质量评定。

A multi-modal welding defect identification method was proposed.A convolutional neural network with three branches was constructed to process welding pool pictures,arc sound,welding current and arc voltage,respectively.A channel attention module and a spatial attention module were added to image branch network to focus on important areas of weld pool image.In order to verify stability and reliability of the model in this paper,experiments were carried out on a self-constructed dataset containing 10 welding defects.The experimental results demonstrated that performance of introducing attention mechanism into front layers was better than that of back layers.Furthermore,attention mechanism contributed to promoting F value,which provided a reference for real-time welding classification and identification and was helpful for welding quality assessment.