Research on real-time road scene segmentation algorithm based on improved BiSeNet
The image information in unmanned scenes is characterized by many kinds of targets and varied scenes,which makes the segmentation task more difficult.In view of the poor effect of direct feature fusion of spatial and semantic information,and the rough segmentation results when facing complex traffic scenes,BiSeNet based on two-branch network is optimized and improved.By adopting a lightweight backbone network and introducing mixed depth convolution,the segmentation accuracy is improved while reducing the computing cost;for the problems of blurred edges and poor segmentation of small targets in image segmentation of complex road scenes,the coordinate attention mechanism is introduced in the context path to obtain more contextual information and improve the segmentation accuracy;the feature fusion module is redesigned,and the feature fusion module is constructed using the attention mechanism-driven method to narrow the size of the feature fusion module and reduce the size of the feature fusion module.The feature fusion module is redesigned to use the attention mechanism driven method to construct the feature fusion module,which reduces the gap between the layers of spatial information and semantic information.The improved algorithm is trained and tested on the CamVid dataset,and the average cross ratio of the algorithm reaches 69.9%.Although the segmentation speed of the improved network is slightly reduced,the average crossover ratio of its training results is improved by 1.6%compared to BiSeNet.