改进型卷积神经网络焊点缺陷识别算法研究

为了同时对多种焊点缺陷类型进行快速识别，解决现有焊接异常图像识别算法误检率与漏检率偏高的问题，设计了基于改进型卷积神经网络的深度学习算法。利用自组织映射分类技术，提高了卷积神经网络的数据选择自适应性，结合自适应矩估计分析, 约束了焊接异常图像中特征集合的收敛条件。实验中将5种常见焊接异常图像以等比例随机分布的形式放入训练集、验证集和测试集中，再分别用传统识别算法(canny算法和k均值算法)和该算法进行测试。结果表明，对于桥连缺陷，3种方法均无误检、无漏检；对于小球缺陷，3种方法均符合要求，而canny算法的检出能力最优；对于偏球缺陷, 3种算法的误检率分别是12.4%, 7.3%和与1.4%，漏检率分别是13.3%, 6.5%和1.1%；对于虚焊和少锡缺陷，该算法相比传统算法精度高约1个数量级。该算法在对多种焊点缺陷类型识别中具有明显优势。

Research on solder joint defect recognition algorithm based on improved convolutional neural network

In order to quickly identify a variety of solder joint defect types and solve the problem of high false detection rate and missed detection rate of traditional welding abnormal image recognition algorithms, a deep learning algorithm based on an improved convolutional neural network was designed. The self-organizing map classification technology improves the data selection adaptability of the convolutional neural network. At the same time, it combines the adaptive moment estimation analysis to restrict the convergence conditions of the feature set in the welding abnormal image. In the experiment, five kinds of common welding anomaly images were randomly distributed into the training set, verification set, and test set in the form of a random distribution of equal proportions. They were tested by traditional recognition algorithms (canny algorithm and k-means algorithm) and this deep learning algorithm, respectively. The results show that, three methods have no false detection and no missed detection for bridge defects. Three methods meet the requirements for small ball defects, and the detection ability of the canny algorithm is the best. For partial ball defects, the false detection rates of three algorithms are 12.4%, 7.3%, and 1.4%, and the missed detection rates of three algorithms are 13.3%, 6.5%, and 1.1%, respectively. For virtual soldering and tin-less defects, the accuracy of this algorithm is about an order of magnitude higher than that of traditional algorithms. It can be seen that this algorithm has obvious advantages in identifying multiple types of solder joint defects.