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.
