HRNet Image Semantic Segmentation Algorithm Combined with Attention Mechanism

doi:10.3969/j.issn.1006-2475.2023.10.010

Abstract

Abstract: Abstract: The current mainstream semantic segmentation algorithms still have problems such as loss of small-sized objects and inaccurate segmentation. In response to these problems， this paper improves the HRNet network model and integrates the attention mechanism to generate more effective feature maps. To address the problem of insufficient detail of the feature map caused by the direct fusion of the low resolution images to the high-resolution images in the original model， a multi-level upsampling mechanism is proposed to make the fusion between images of different resolutions smoother to achieve better fusion results， and the depth separable convolution is used to reduce the parameters of the model. The model in this article maintains a high resolution of the image throughout the entire process. The spatial information of the feature map is improved， and the segmentation effect of small-sized objects is improved. The mIoU value on the PASCAL VOC2012 enhanced dataset reaches 80.87%， and the accuracy is improved by 1.54 percentage points compared with the original model.

Key words: Key words: image semantic segmentation, attention mechanism, high resolution, depthwise separable convolution

CLC Number:

TP391

YE Si-jia, WEI Yan, DU Han-yu, DENG Jin-zhi. HRNet Image Semantic Segmentation Algorithm Combined with Attention Mechanism[J]. Computer and Modernization, 2023, 0(10): 65-69.

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