A semantic big data analysis method based on enhanced neural networks in IoT

Abstract

Due to the growth of neural networks, the semantic big data analysis method can classify images at the pixel level, which is very suitable for the needs of IoT. In semantic big data analysis methods, the DeepLab algorithm is an improved and highly accurate algorithm based on enhanced neural networks. However, the DeepLab algorithm does not fully utilize global information, resulting in poor performance for complex scenes. Therefore, this article makes improvements by introducing a global context information module and providing prior information of complex scenes in images. It extracts global information and merges with original features. It improves the expression ability of features. This global context can enhance the accuracy of semantic big data analysis method, and an attention mechanism is designed. The experimental results display that the improved DeepLab semantic big data analysis method based on self-attention and global context module has good average pixel accuracy and average intersection to union ratio performance on the Pascal VOC 2012 dataset. And the improvement effect is significant.