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Domain adaptive segmentation method for mechanical assembly based on iterative loops
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DOI：10.1007/s10489-024-05931-y.png)
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摘要
En 中文
During the assembly process of mechanical products, employing deep learning techniques for the semantic segmentation of assembly images enables real-time monitoring of irregularities, including incorrect or missing assemblies. However, most of the current monitoring methods based on deep learning adopt supervised learning. This requires a large number of labels according to different assembly specifications, which is time-consuming and laborious. To address this issue, this study designed a two-stage adaptive segmentation framework based on iterative loops for synthesis-physical assembly images, i.e., ILDA-Net (iterative loops domain adaptation network), which does not require any labeling of physical assemblies. In the adversarial learning stage, a trainable line-guided filter module and a line discriminator module are introduced for maintaining line features. The two modules are iteratively trained in a loop to continuously optimize the segmentation model. In the self-training stage, the edge segmentation quality is guaranteed by optimizing the segmentation model through utilizing unreliable pseudo-labels. Finally, this study constructed a set of semantic segmentation datasets for domain adaptation of synthetic-physical assembly images and conducted experiments on these datasets. Based on these experiments, the Dice coefficient can reach up to 89.33%, which demonstrating that the proposed method can be utilized for the physical assembly image segmentation.
Keyword:
Assembly monitoring
Semantic segmentation
Domain adaptation
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
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