  Image semantic segmentation technology is one of the key technologies for
intelligent systems to understand natural scenes. As one of the important
research directions in the field of visual intelligence, this technology has
broad application scenarios in the fields of mobile robots, drones, smart
driving, and smart security. However, in the actual application of mobile
robots, problems such as inaccurate segmentation semantic label prediction and
loss of edge information of segmented objects and background may occur. This
paper proposes an improved structure of a semantic segmentation network based
on a deep learning network that combines self-attention neural network and
neural network architecture search methods. First, a neural network search
method NAS (Neural Architecture Search) is used to find a semantic segmentation
network with multiple resolution branches. In the search process, combine the
self-attention network structure module to adjust the searched neural network
structure, and then combine the semantic segmentation network searched by
different branches to form a fast semantic segmentation network structure, and
input the picture into the network structure to get the final forecast result.
The experimental results on the Cityscapes dataset show that the accuracy of
the algorithm is 69.8%, and the segmentation speed is 48/s. It achieves a good
balance between real-time and accuracy, can optimize edge segmentation, and has
a better performance in complex scenes. Good robustness is suitable for
practical application.
