Image Semantic Segmentation Algorithm Based on Adaptive Fusion of Multi-scale Features
WANG Zhen1,YANG Jun2,DENG Jia-li1,XIE Hong-hui2,HUANG Cong2
1(School of Computer and Information Engineering,Jiangxi Agriculture University,Nanchang 330045,China)2(School of Software,Jiangxi Agriculture University,Nanchang 330045,China)
Abstract：In order to solve the problem that the Deeplab v3+ only fuses one scale encoding feature in the decoding process,which leads to the loss of some detailed information and causes the final segmentation result to be rougher,a image semantic segmentation algorithm based on adaptive fusion of multi-scale features is proposed.The algorithm uses an adaptive spatial feature fusion structure to assign adaptive fusion weights to encoding features of different scales in the decoding process of Deeplab v3+,and upsamples the feature map by fusing the multi-scale features in the encoding process to achieve a more refined image semantic segmentation result.Experimental results show that the algorithm achieves 95.05% pixel accuracy and 69.36% mean intersection over union on Cityscapes,and segmentation of most small-scale target objects is more accurate.On the Vaihingen dataset the proposed algorithm achieves a pixel accuracy of 83.49% and a mean intersection over union of 68.77%,which further verifies the generalization of the algorithm.
2022, Vol. 43 
