Semantic Segmentation under a Complex Background for Machine Vision Detection Based on Modified UPerNet with Component Analysis Modules

Abstract

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.