Improved ASPP and Multilevel Feature Semantic Fusion Segmentation Method

To solve the problems of the difficult multi-scale target segmentation and inaccurate category boundary prediction in image semantic segmentation, a multilevel feature semantic fusion segmentation method based on improved atrous spatial pyramid pooling is proposed. Firstly, the deep-level network features are grouped by the channels, and the multi-scalefeature context information of each grouped is captured by using the split atrous spatial pyramid pooling module. Secondly, the strip pooling module is introduced to supplement and refine the contextual information and enhance the global semantic information representation. Finally, the semantic guidance fusion module is used to establish the correspondence between the feature pixels at different levels, and the deep-level semantic information is gradually incorporated into the low-level high-resolution image with a bottom-up manner. The experimental results show that this method obtains 73.1% and 71.8% of the mean intersection over union on the PASCAL VOC 2012 and Cityscapes public datasets, respectively, and reduces the number of parameters by 39% with the same accuracy. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.