Mask-RCNN with spatial attention for pedestrian segmentation in cyber-physical systems

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-MaskRCNN 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.