The degree of penetration can directly reflect the forming quality of laser welding. The fine-grained feature of the molten pool/keyhole image brings challenges to the vision-based laser welding penetration status rec...The degree of penetration can directly reflect the forming quality of laser welding. The fine-grained feature of the molten pool/keyhole image brings challenges to the vision-based laser welding penetration status recognition. In this paper, a novel knowledge-and-data-hybrid driven recognition model is proposed for solving the problem of difficult learning of discriminative visual features of molten pool/keyhole images. In addition, a label semantic attention mechanism(LSA) is designed with three modules: representation of image visual feature, representation of labels semantic feature, and generation of label semantic attention. For learning discriminative features in visual space, LSA uses discriminative information in label semantics to guide the convolutional neural network. The experimental results show that the proposed LSA method has faster convergence and higher accuracy than the traditional attention mechanism. Further comparative experiments reveal that LSA is less dependent on the amount of training data and model complexity. The results of visualization experiments show that the visual features learned by the proposed method are more discriminative.展开更多
基金supported by the Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University(Grant No.CUSF-DH-D-2020053)。
文摘The degree of penetration can directly reflect the forming quality of laser welding. The fine-grained feature of the molten pool/keyhole image brings challenges to the vision-based laser welding penetration status recognition. In this paper, a novel knowledge-and-data-hybrid driven recognition model is proposed for solving the problem of difficult learning of discriminative visual features of molten pool/keyhole images. In addition, a label semantic attention mechanism(LSA) is designed with three modules: representation of image visual feature, representation of labels semantic feature, and generation of label semantic attention. For learning discriminative features in visual space, LSA uses discriminative information in label semantics to guide the convolutional neural network. The experimental results show that the proposed LSA method has faster convergence and higher accuracy than the traditional attention mechanism. Further comparative experiments reveal that LSA is less dependent on the amount of training data and model complexity. The results of visualization experiments show that the visual features learned by the proposed method are more discriminative.
