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The Improved K-nearest Neighbor Solder Joints Defect Detection

机译：改进的k最近邻焊接接头缺陷检测

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摘要

Aiming at the problems such as defect misstatements, omissions are prone to happen when automatic optical inspection (AOI) system detects Printed Circuit Board (PCB) solder joints. The article puts forward a kind of method based on improved K-nearest neighbor to test and classify the quality of solder joints. Firstly, the original images collected by industrial camera should be pretreated, and solder joints should be positioned by using the method of template matching. Secondly, the features of solder joints should be extracted and selected usefully through the experiments. Finally, the improved K-nearest neighbor algorithm based on effective feature is used to test and classify solder joints. Experiments show that the improved K-nearest neighbor algorithm has higher accuracy and stronger adaptability than neural network algorithm used for classification. What's more, the cost of testing is also reduced effectively. So we can conclude that the improved K-nearest neighbor algorithm is useful for solder joints testing.
机译：针对缺陷错误错误等问题，当自动光学检查（AOI）系统检测到印刷电路板（PCB）焊点时，遗漏易于发生。本文提出了一种基于改进的K-Collect邻居的方法来测试和分类焊点的质量。首先，应该预处理由工业相机收集的原始图像，并且应该使用模板匹配方法定位焊点。其次，应通过实验提取和选择焊点的特征。最后，使用基于有效特征的改进的K最近邻算法用于测试和分类焊点。实验表明，改进的K-最近邻算法具有比用于分类的神经网络算法更高的准确度和更强的适应性。更重要的是，测试成本也有效减少。因此，我们可以得出结论，改进的K-最近邻算法对于焊接接头测试是有用的。

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