Improved Semantic Segmentation Model and Its Application

The training of semantic segmentation model requires complicated manual labeling, and there are also some problems in the construction and operation of semantic segmentation model, such as determining its hyperparameters and becoming bloated. To solve these problems, this paper proposes a label generation method based on heat map generated by ground truth box, which simplifies the manual labeling process of semantic segmentation training labels. A neural architecture search method with lower hardware requirements is proposed, which based on the differentiable neural architecture search method. By this method, the improved semantic segmentation model which contains a new feature pyramid is constructed. Tested on the helmet and mask detection datasets, compared with U-NET, FPN and other models, the new model takes the advantages in the number of parameters, calculation speed and accuracy. © 2024 Editorial Office of Tunnel Construction. All rights reserved.