Aiming at the problem of the poor robustness of existing methods to deal with diverse industrial weld image data, we collected a series of asymmetric laser weld images in the largest laser equipment workshop in Asia, and studied these data based on an industrial image processing algorithm and deep learning algorithm. The median filter was used to remove the noises in weld images. The image enhancement technique was adopted to increase the image contrast in different areas. The deep convolutional neural network (CNN) was employed for feature extraction; the activation function and the adaptive pooling approach were improved. Transfer Learning (TL) was introduced for defect detection and image classification on the dataset. Finally, a deep learning-based model was constructed for weld defect detection and image recognition. Specific instance datasets verified the model’s performance. The results demonstrate that this model can accurately identify weld defects and eliminate the complexity of manually extracting features, reaching a recognition accuracy of 98.75%. Hence, the reliability and automation of detection and recognition are improved significantly. The research results can provide a theoretical and practical reference for the defect detection of sheet metal laser welding and the development of the industrial laser manufacturing industry.

中文翻译：

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基于深度学习模型的工业激光焊接缺陷检测与图像缺陷识别

针对现有方法处理多样化工业焊缝图像数据鲁棒性差的问题，我们在亚洲最大的激光设备车间收集了一系列非对称激光焊缝图像，并基于工业图像处理算法和深度学习算法。中值滤波器用于去除焊接图像中的噪声。采用图像增强技术来增加不同区域的图像对比度。采用深度卷积神经网络（CNN）进行特征提取；改进了激活函数和自适应池化方法。引入了转移学习（TL）用于数据集上的缺陷检测和图像分类。最后，构建了基于深度学习的焊接缺陷检测和图像识别模型。特定的实例数据集验证了模型的性能。结果表明，该模型能够准确识别焊缝缺陷，消除人工提取特征的复杂性，识别准确率达到98.75%。因此，显着提高了检测和识别的可靠性和自动化程度。研究成果可为钣金激光焊接缺陷检测及工业激光制造行业的发展提供理论和实践参考。