Contrastive and transfer learning-based visual small component inspection in assembly

Abstract

Deep learning-based visual quality inspection methods have been increasingly adopted in product assembly process. However, the inspection for small component remains a challenging issue due to the small size and difficulty in feature extraction. Meanwhile, due to the large number of assembly scenarios of small components, the dependency on abundant manually labeled training dataset limits the further application of these inspection methods. To mitigate the situation, this paper designs a contrastive and transfer learning-based visual inspection approach to inspect the assembly state of small component. An attention-guided lightweight feature extraction and reconstruction encoder is designed to extract features of small component, of which the deconvolution operation is leveraged to recover the resolution of the feature map, and the bidirectional feature pyramid network algorithm is introduced to effectively fuse multi-scale feature information. Moreover, an unsupervised contrastive pre-training method for the designed encoder is proposed to effectively learn the visual representation of small component. With the use of a little annotated data, the pre-trained encoder can be flexibly fine-tuned and transferred to different small component assembly scenarios. The comparative experiments demonstrate the effectiveness of our method.