基于机器视觉的焊点检测算法研究

摘要

为了提高电路板焊点检测的准确率,提出了改进的K-近邻法.首先,采用工业相机采集图像并选取470个焊点作为训练样本,利用模板匹配法对图像中的焊点进行定位.然后根据特征分布直方图提取焊点的特征并绘制特征分布情况,选择能区分不同类别焊点的特征作为有效特征.最后,建立改进的K-近邻法焊点检测分类器,选取559个焊点作为测试样本对模型进行测试.实验结果表明改进的K-近邻算法检测的准确率96%以上,可以有效地提高检测效率.%In order to improve the detection accuracy of circuit board solder joints,an improvement of K-nearest neighbor method was proposed.Firstly,the industrial camera was used to obtain images,and 470 solder joints were selected as the training samples.The template matching method was used to position the solder joints in the images.Then,the features of solder joints were extracted according to the feature distribution histogram.The space distribution of features was portrayed and then selected the festures can distinguish between solder joints of different types.Finally,the improved K-nearest method classifier was setted up and 559 solder joints were selected as the testing samples to test the model.The experimental results show that the improved K-nearest neighbor method detection accuracy rate reach as high as 96% and can effectively improve the efficiency of detection.