M-FasterSeg: An Efficient Semantic Segmentation Network Based on Neural Architecture Search
This work addresses real-time semantic segmentation for mobile robots, drones, and smart driving, but it is incremental as it builds on existing NAS and self-attention methods.
The paper tackles inaccurate semantic label prediction and edge information loss in semantic segmentation for mobile robots by proposing M-FasterSeg, an improved network combining self-attention and neural architecture search, achieving 69.8% accuracy and 48 frames per second on the Cityscapes dataset.
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.