基于深度学习的零件实例分割识别研究

针对传统零件识别方法图像特征提取鲁棒性不足,零件识别准确率较低、不能对图像进行实例分割的问题,文章提出了一种基于Mask R-CNN的零件识别方法。该方法利用卷积神经网络对零件图像进行特征提取,选取数据集中标注好的图像微调Mask R-CNN网络,以保证零件识别的准确性,并生成Mask分割掩码,对零件进行实例分割。同时,对数据集进行数据增强和划分K折交叉验证来提高模型的鲁棒性。最后通过搭建实验平台对零件进行识别,证明了该方法的有效性。

Research on Part Instance Segmentation and Recognition Based on Deep Learning

Aiming at the problem that the image feature extraction of traditional part recognition method is not robust enough, the accuracy of part recognition is low, and the image cannot be segmented by example. This paper proposes a part recognition method based on Mask R-CNN. The method uses the convolutional neural network to extract the features of the part image, selects the annotated part image from dataset to fine-tuned Mask R-CNN to guarantee the accuracy of the part recognition, and generates the segmentation mask to segment the parts. Data expansion and K-Fold Cross Validation also were used to improve the robustness of the model. Finally, the result of part recognition by building the experimental platform proves the effectiveness of the method.