结合全卷积网络和K均值聚类的球栅阵列焊球边缘气泡分割

针对在球栅阵列（BGA）气泡检测中，由于图像干扰因素的多样性导致焊球存在边缘气泡与背景之间灰度级接近，从而造成焊球气泡分割结果不精确的问题，提出了一种结合全卷积神经网络（FCN）和K均值（K-means）聚类分割的焊球气泡分割方法。首先根据所制作的BGA标签数据集搭建FCN，通过训练该网络得到合适的网络模型，再对待测BGA图像进行预测处理得到图像的粗分割结果；然后对焊球区域映射提取，通过同态滤波法提高气泡区域辨识度，再使用K-means聚类分割对图像进行细分割处理，得到最终分割结果图；最后对原图焊球及气泡区域进行标注识别。将所提出的算法与传统BGA气泡分割算法进行对比，实验结果表明，所提出的算法对复杂BGA焊球的边缘气泡分割精确，图像分割结果与其真实轮廓高度匹配，准确度更高。

Welding ball edge bubble segmentation for ball grid array based on full convolutional network and K-means clustering

For inaccurate segmentation results caused by the existence of edge bubbles in welding balls and the grayscale approximation of background due to the diversity of image interference factors in Ball Grid Array (BGA) bubble detection, a welding ball bubble segmentation method based on Fully Convolutional Network (FCN) and K-means clustering was proposed. Firstly, a FCN network was constructed based on the BGA label dataset, and trained to obtain an appropriate network model, and then the rough segmentation result of the image were obtained by predicting and processing the BGA image to be detected. Secondly, the welding ball region mapping was extracted, the bubble region identification was improved by homomorphic filtering method, and then the image was subdivided by K-means clustering segmentation to obtain the final segmentation result. Finally, the welding balls and bubble region in the original image were labeled and identified. Comparing the proposed algorithm with the traditional BGA bubble segmentation algorithm, the experimental results show that the proposed algorithm can segment the edge bubbles of complex BGA welding balls accurately, and the image segmentation results highly match the true contour with higher accuracy.