Semantic Segmentation Method Based on Residual and Multi-Scale Feature Fusion

In order to solve the problem of insufficient use of context information and lack of detail information in image semantic segmentation, this paper proposes an RMFU network model based on residual module and multi-scale feature fusion. RMFU network uses cross entropy and mean intersection over union as loss function. RMFU network obtains the fine semantic segmentation results by using fully connected Conditional Random Field to optimize the initial segmentation results. The test results on Pascal VOC 2012 dataset show that RMFU network is better than FCN network in SSIM and COSIN. RMFU network effectively uses multi-scale feature information. It improves the precision of image semantic segmentation and optimizes the image segmentation effect.