Instance Segmentation of Mechanical Assembly Image Based on TD-Mask R-CNN
In the assembly process of mechanical products,in order to accurately identify the mechanical parts information to reduce the phenomenon of parts leakage and misassembly,this paper proposes an im-proved instance segmentation method TD-Mask R-CNN for mechanical assemblies images.First,deformable convolutional networks(DCN)is introduced into the backbone network ResNet101 to increase the general-ization ability of the network model.Then,the Transfiner structure is used as a mask branch to improve the segmentation accuracy of the mechanical part edges.Finally,the discrete cosine transform(DCT)module is introduced in the Transfiner structure to improve the model's ability to segment the whole image of the mechanical assembly.The experimental results show that the average precision(AP)of the mask obtained by this method on the synthetic depth image dataset and the real color image dataset are 87.7％ and 92. 0％ ,respectively,which is improved compared with other mainstream instance segmentation algorithms.
deep learningassembly monitoringinstance segmentationTD-Mask R-CNN
NETL
NSTL
万方数据
维普
