Solder Joint Recognition Using Mask R-CNN Method

Abstract

This article proposes a novel solder joint recognition method based on the state-of-the-art Mask Region-convolutional neural network (R-CNN) deep learning method. Traditional classification methods, such as neural networks and statistical methods, can only classify defect type, and the template-matching method can only match the position of the object. Based on Mask R-CNN, our proposed approach can classify, position, and segment the solder joint defect at the same time. To train our Mask R-CNN-based detection method, the transfer learning method uses the ResNet-101, which is initialized and trained on the Microsoft COCO data set. Through experimentation, our proposed method obtained better results than the traditional classification method in solder joint recognition, and it can achieve very high classification accuracy with more than 95% mean of average precision (mAP) for segmentation. The proposed method can classify and identify the position and segment of the solder joint defect simultaneously with very high recognition accuracy.