Upernet optimisation and application to mousehole segmentation

Kai Li, Bingming Tang, Haiyang Li, Yunpeng Jin, Jieteng Jiang, L. Chunmei
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Abstract

The evaluation of grassland degradation is an important part of ecological conservation research, and rodent infestation is a significant factor in grassland degradation. The presence of a large number of mouseholes means that the environmental balance of grassland has been destroyed, so the coverage of mouseholes can be used as an evaluation method for grassland degradation levels. In this paper, the image segmentation method is used to segment the mousehole images, Upernet is used as the segmentation network, and Swin Transformer as the Backbone. FAM and FSM modules are added to the Upernet network to solve the target misalignment problem when upsampling the network. The mIoU is improved by 5.3% according to the experimental results.
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超级优化及其在鼠孔分割中的应用
草地退化评价是生态保护研究的重要组成部分,鼠害是草地退化的重要影响因素。鼠洞的大量存在意味着草地的环境平衡已经被破坏,因此鼠洞覆盖率可以作为草地退化程度的一种评价方法。本文采用图像分割的方法对老鼠洞图像进行分割,以Upernet作为分割网络,Swin Transformer作为主干网。在Upernet网络中增加FAM和FSM模块,解决网络上采样时目标不对准的问题。实验结果表明,mIoU提高了5.3%。
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