Transfer learning is a promising method for AOI applications since it can significantly shorten sample collection time and improve efficiency in today’s smart manufacturing. However, related research enhanced the network models by applying TL without considering the domain similarity among datasets, the data long-tailedness of a source dataset, and mainly used linear transformations to mitigate the lack of samples. This research applies relational-based TL via domain similarity to improve the overall performance and data augmentation in both target and source domains to enrich the data quality and reduce the imbalance. Given a group of source datasets from similar industrial processes, we define which group is the most related to the target through the domain discrepancy score and the number of samples each has. Then, we transfer the chosen pre-trained backbone weights to train and fine-tune the target network. Our research suggests increases in the F1 score and the PR curve up to 20% compared with TL using benchmark datasets.
Image super-resolution technology successfully overcomes the limitation of excessively large pixel size in infrared detectors and meets the increasing demand for high-resolution infrared image information. In this paper, the superresolution reconstruction of infrared images based on a convolutional neural network with a priori for high frequency information is reported. The main network structure is based on residual blocks, BN blocks that are not suitable for the super-resolution task are removed. The introduction of residual learning reduces computational complexity and accelerates network convergence. Multiple convolution layers and deconvolution layers respectively implement the extraction and restoration of the features in infrared images. images are divided into high frequency and low frequency parts. The low frequency part is the image of down-sampling, while the high frequency information is obeyed a simple case-agnostic distribution, which is equivalent to having a prior of high frequency information for the super-resolution network, Which is captures some knowledge on the lost information in the form of its distribution and embeds it into model’s parameters to mitigate the ill-posedness. Compared with the other previously proposed methods for infrared information restoration, our proposed method shows obvious advantages in the ability of high-resolution details acquisition.
For solving the problem of sub-mirror installation and posture monitoring and compensation, an absolute four-degree-of-freedom (DOF) grating encoder that is able to monitor four degrees of freedom's absolute position and pose in the θx, θy, θz, and z-direction is proposed. In this grating encoder, a grating reflector and three quadrant photodetectors (QPD) are employed and an optical path is configured based on the laser autocollimation principle. A model for the solution of the four-DOF motions from outputs of the three QPDs is established. A calibration method for the identification of the relationships between the absolute positions and QPDs outputs is proposed. A prototype four-DOF grating encoder is constructed for verification of this proposal. Test results demonstrated that the method proposed in this research can achieve absolute position distinguishing with a sub-arcsecond and sub-micrometer accuracy in rotation angles and z-direction, respectively.
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