Research on Semantic Segmentation Algorithm for Multiscale Feature Images Based on Improved DeepLab v3+

Abstract

Aiming at the question of erroneous segmentation and missed segmentation that occurs when the DeepLab v3+ model cannot fully utilize high-resolution shallow features, a feature image semantic segmentation algorithm based on improved DeepLab v3+ with multiscale features is proposed. First, in the backbone network, multi-scale pyramid convolution is introduced; second, the standard convolution in the pooled pyramid of null-space convolution has been taken by depth-separable convolution, which declines the number of parameters in the overall model; finally, a multi-scale approach is used in the decoding layer to capture the acquired global background, and the background features are combined with the shallow features, and the fused shallow features are enriched by the fusion-attention mechanism to provide the semantic information for images. The experimental results show that in the cityscape validation set, this paper’s method has a better edge segmentation effect, and the average cross-merge rate reaches 74.76%, which is 2.20% higher than the original algorithm. By comparing with other algorithms, the effectiveness of this paper’s method in improving mis-segmentation and omission segmentation is verified.