Multi-feature fusion network for road scene semantic segmentation

Road scene semantic segmentation often requires a deeper neural network to obtain higher accuracy, which makes the segmentation model more complex and slower. In this paper, we use shallow neural networks to achieve semantic segmentation for intelligent transportation system. Specifically, we propose a lightweight semantic segmentation model. First, the image features are extracted by using a simple superimposed convolutional layer and the three branches of ResNet and optimized by the attention mechanism. Then element multiplication and feature fusion are performed. Finally, the segmentation mask is obtained. Fewer convolutional layers and ResNet will not take up a lot of resources, we use the main resources to calculate the fusion between features. Experiments show that our method achieves high accuracy and comparable speed on the Cityscapes and CamVid datasets. On the Cityscapes dataset, our method achieves 75.0% mIoU, which is 0.2% higher than the better-performing BiSeNet.

Automated summary

This paper introduces a lightweight semantic segmentation model for intelligent transportation system, utilizing shallow neural networks. By simplifying the model structure and feature fusion methods, the approach achieves high accuracy while maintaining fast speed.