Road Scene Semantic Segmentation Model Based on Multi-Scale Attention Mechanism

Abstract

Semantic segmentation of road scenes can assist vehicles to perceive the surrounding environment, to avoid pedestrians, vehicles and all kinds of small object obstacles, and further improve the safety of driving.This study proposes a semantic segmentation network based on multi-scale attention mechanism, aiming at the problems of low recognition accuracy of small objects in semantic segmentation of road scene in deep learning, and the large number of network parameters adversely affecting the deployment.A multi-scale wavelet attention module is designed based on the characteristics of wavelet transform with multi-scale and multi frequency information analysis and embedded into the encoder structure.By fusing the characteristics of different scales and frequencies, more edge contour details are retained.The hierarchical connection between the encoder and the decoder and the improved pyramid pooling module are used for feature extraction in many aspects to obtain more image details, while retaining the context feature information.By designing the training model of multistage loss function, the network convergence is accelerated.The experimental results on the Cambridge-driving Labeled Video Database(CamVid) show that the average intersection and merge ratio of the model is 60.21%, which reduces the parameters by nearly 30% compared with DeepLabV3+ and DenseASP models.The segmentation accuracy of this model is improved without additional parameters, and the model has good robustness in different scenes.