Weld defect recognition plays an important role in the manufacturing process of large-scale equipment. Traditional methods generally include several serial steps, such as image preprocessing, region segmentation, feature extraction, and type recognition. The results of each step have significant impact on the accuracy of the final defect identification. The convolutional neural network (CNN) has strong pattern recognition ability, which can overcome the above problem. However, there are two problems: one is that the pooling strategy has poor dynamic adaptability and the other is the insufficient feature selection ability. To overcome these problems, we propose a CNN-based weld defect recognition method, which includes an improved pooling strategy and an enhanced feature selection method. According to the characteristics of the weld defect image, an improved pooling strategy that considers the distribution of the pooling region and feature map is introduced. Additionally, in order to enhance the feature selection ability of the CNN, an enhanced feature selection method integrating the ReliefF algorithm with the CNN is proposed. A case study is presented for demonstrating the proposed techniques. The results show that the proposed method has higher accuracy than the traditional CNN method, and establish that the proposed CNN-based method is successfully applied for weld defect recognition.

焊接缺陷识别在大型设备的制造过程中起着重要作用。传统方法通常包括几个连续步骤，例如图像预处理，区域分割，特征提取和类型识别。每个步骤的结果都会对最终缺陷识别的准确性产生重大影响。卷积神经网络（CNN）具有强大的模式识别能力，可以克服上述问题。但是，存在两个问题：一个是池化策略的动态适应性较差，另一个是特征选择能力不足。为了克服这些问题，我们提出了一种基于CNN的焊接缺陷识别方法，其中包括改进的合并策略和增强的特征选择方法。根据焊缝缺陷图像的特征，引入了一种考虑池区域和特征图分布的改进池策略。另外，为了增强CNN的特征选择能力，提出了一种将ReliefF算法与CNN集成的增强特征选择方法。提出了一个案例研究，以演示所提出的技术。结果表明，该方法比传统的CNN方法具有更高的准确性，并表明该基于CNN的方法已成功应用于焊接缺陷识别。提出了一个案例研究，以演示所提出的技术。结果表明，该方法比传统的CNN方法具有更高的准确性，并表明该基于CNN的方法已成功应用于焊接缺陷识别。提出了一个案例研究，以演示所提出的技术。结果表明，该方法比传统的CNN方法具有更高的准确性，并表明该基于CNN的方法已成功应用于焊接缺陷识别。