Multi-scale image semantic segmentation based on ASPP and improved HRNet

Aiming at the problems of huge model and multi-scale object segmentation in the classical semantic segmentation algorithm, an efficient multi-scale image semantic segmentation method based on ASPP and HRNet is proposed. Firstly, the basic block is improved by using deep separable convolution combined with 1 * 1 convolution to reduce the model parameters. Secondly, a batch normalization layer (BN) is added after all convolution layers and before the relu activation function to improve the dead relu problem. Finally, the improved ASPP module based on the hybrid dilated convolution is added, and the advantages of the two are fused by using the parallel upsampling channels to obtain the spatial accurate segmentation results. The RE-ASPP-HRNet is proposed. Results on Pascal voc2012 and CityScapes show that the proposed method is effective. Compared with the original HRNet, it can improve the accuracy of 0.8% or 0.5% MIoU, and reduce the number of parameters by 1/2 and memory by 1/3. We implement a more efficient and reliable multi-scale semantic segmentation algorithm.