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An Explainable Laser Welding Defect Recognition Method Based on Multi-Scale Class Activation Mapping
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DOI：10.1109/TIM.2022.3148739.png)
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Abstract
En 中文
Vision-based online defect recognition can provide insights for laser welding quality control systems. Although the visual signal contains richer quality information than the one-dimensional signal, the quality features contained in the visual signal are more abstract. To improve the explainability of current convolutional neural networks (CNNs) for laser welding defect recognition (LWDR), a class activation mapping method based on multi-scale fusion features (CAM-MSFF) is proposed. In addition, a multi-scale features adaptive fusion method is proposed with three steps of feature squeeze, feature mapping, and feature recalibrating. In order to facilitate the learning and utilization of multi-scale features by the proposed method, supervisory information is applied to multiple scales. The experimental results show that the proposed CAM-MSFF method has higher accuracy and convergence speed than the conventional model. The results of the explainability tests show that the proposed method can provide a more accurate and human-comprehensible explanation of the model & x2019;s decision basis.
Keywords:
Welding
Feature extraction
Visualization
Convolutional neural networks
Lasers
Adaptation models
Sensors
Class activation mapping (CAM)
convolutional neural network (CNN)
defect recognition
explainable deep learning (XDL)
laser welding
multi-scale
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5.9
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2.0W
Citations:
5.8W
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