Image semantic segmentation with hierarchical feature fusion based on deep neural network

Abstract

In the field of image segmentation in industrial production. When using depth neural network to extract features from image information. Deep neural network can not effectively use the feature information between different levels. The accuracy of image semantic segmentation is damaged. To solve this problem, we present an image semantic segmentation with hierarchical feature fusion based on deep neural network (ISHF). It can be widely used in image perception and recognition of various living and industrial backgrounds. This algorithm uses convolution structure to extract the shallow low-level features with pixel level and the deep semantic features with image level, fully obtaining the hidden feature information in the shallow low-level features and deep semantic features. Then, after thinning the shallow low-level feature information by up-sampling operation, all feature information is merged and fused. Finally, the image semantic segmentation of hierarchical feature fusion is realised. Experimental results shows that our proposed method not only get better performance of image semantic segmentation, but also achieve faster running speed than SegNet, PSPNet, and DeepLabV3.