Semantic segmentation with convolutional neural networks under a complex background using the encoder-decoder network increases the overall performance of online machine vision detection and identification. To maximize the accuracy of semantic segmentation under a complex background, it is necessary to consider the semantic response values of objects and components and their mutually exclusive relationship. In this study, we attempt to improve the low accuracy of component segmentation. The basic network of the encoder is selected for the semantic segmentation, and the UPerNet is modified based on the component analysis module. The experimental results show that the accuracy of the proposed method improves from 48.89% to 55.62% and the segmentation time decreases from 721 to 496 ms. The method also shows good performance in vision-based detection of 2019 Chinese Yuan features.

Image deblurring can improve visual quality and mitigates motion blur for dynamic visual inspection. We propose a method to deblur saturated images for dynamic visual inspection by applying blur kernel estimation and deconvolution modeling. The blur kernel is estimated in a transform domain, whereas the deconvolution model is decoupled into deblurring and denoising stages via variable splitting. Deblurring predicts the mask specifying saturated pixels, which are then discarded, and denoising is learned via the fast and flexible denoising network (FFDNet) convolutional neural network (CNN) at a wide range of noise levels. Hence, the proposed deconvolution model provides the benefits of both model optimization and deep learning. Experiments demonstrate that the proposed method suitably restores visual quality and outperforms existing approaches with good score improvements.

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