An important issue in the field of unmanned driving is how to run real-time high-precision semantic segmentation models on low-power mobile electronic devices. Existing semantic segmentation algorithms have too many parameters and huge memory usage

which makes it difficult to meet the problems of real-world applications such as unmanned driving. However

among the many factors that affect the accuracy and speed of the semantic segmentation model

spatial information and contextual features are particularly important

and it is difficult to take into account both. In response to this problem

it is proposed to use the incomplete ResNet18 as the backbone network

design a bilateral semantic segmentation model

and add a channel space dual attention model to the two paths to obtain more contextual and spatial information. In addition

the attention optimization module that refines the context information and the fusion module that integrates the output of the two paths are also used. Take Cityscapes and CamVid as data sets. On Citycapes

mIoU reached 77. 3% ; on CamVid

mIoU reached 66. 5% . When the input image resolution is 1024 × 2048

the segmentation speed is 37. 9 ms.