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.

中文翻译：

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基于带有组件分析模块的改进型UPerNet的复杂背景下的机器视觉检测语义分割

使用编码器-解码器网络在复杂背景下使用卷积神经网络进行语义分割，可以提高在线机器视觉检测和识别的整体性能。为了在复杂背景下最大化语义分割的准确性，有必要考虑对象和组件的语义响应值及其相互排斥的关系。在这项研究中，我们试图提高成分分割的低准确性。选择编码器的基本网络进行语义分割，并基于组件分析模块修改UPerNet。实验结果表明，该方法的精度从48.89％提高到55.62％，分割时间从721ms降低到496ms。