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