Lightweight Real‐Time Image Semantic Segmentation Network Based on Multi‐Resolution Hybrid Attention Mechanism

Abstract

Effective perception of the surrounding environment and the balance between accuracy and processing speed are crucial for the successful application of real‐time semantic segmentation algorithm in the fields of autonomous driving, drones, and smart security. In this paper, a lightweight feature reuse network MHANet for real‐time semantic segmentation is proposed. The main novelties of our method are improved ResNet and attention‐based fusion mechanism. And the effectiveness of our method is verified by a large number of experiments. Without any pre‐training process, the performance of real‐time segmentation is improved by using deep fusion of segmentation maps with different resolutions. At the same time, our network converges faster than other networks using pre‐training when trained from scratch. Compared with existing methods, the results obtained with our method on the Camvid dataset improve in accuracy (mIoU) ranging from 2% to 6% and in efficiency (FPS) ranging from 15% to 18%. The results achieved 71.87% mIoU of accuracy in the Cityscapes test set, processing images at 203 FPS. Experiments show that manual designed MHANet is effective in improving the performance of real‐time semantic segmentation without any pre‐training.