With the application of industrial cyber–physical systems in various fields such as transportation systems, smart cities, and medical systems, pedestrian scenarios are becoming more and more complex, which brings difficulties to pedestrian segmentation. The difficulty of pedestrian segmentation lies in the scene’s complexity where the pedestrian is located, including the pedestrian’s shooting angle, light, and obstructions, which makes it difficult to distinguish accurately. This paper proposes an S-Mask-RCNN network that integrates spatial attention mechanisms for pedestrian segmentation. Mask-RCNN uses residual neural networks in the feature extraction network, and the effect of model feature extraction is not ideal. Based on transfer learning, a spatial attention mechanism is introduced to focus more spatially on areas that need attention. The force mechanism focuses more on the areas that need attention in space. Experiments show that the S-Mask-RCNN method proposed in this paper has better performance than traditional Mask-RCNN in pedestrian segmentation. Experiments show that the S-Mask-RCNN method proposed in this paper has better performance than traditional Mask-RCNN in pedestrian segmentation, also can provide more comprehensive and practical information for the construction of cyber–physical systems.

随着工业信息物理系统在交通系统、智慧城市、医疗系统等各个领域的应用，行人场景变得越来越复杂，这给行人分割带来了困难。行人分割的难点在于行人所处场景的复杂性，包括行人的拍摄角度、光线、障碍物等，难以准确区分。本文提出了一种 S-Mask-RCNN 网络，该网络集成了行人分割的空间注意机制。Mask-RCNN在特征提取网络中使用了残差神经网络，模型特征提取的效果并不理想。在迁移学习的基础上，引入了空间注意力机制，以在空间上更加关注需要注意的区域。力机制更侧重于太空中需要关注的领域。实验表明，本文提出的S-Mask-RCNN方法在行人分割方面比传统的Mask-RCNN具有更好的性能。实验表明，本文提出的S-Mask-RCNN方法在行人分割方面比传统的Mask-RCNN具有更好的性能，也可以为信息物理系统的构建提供更全面、更实用的信息。