Semantic Segmentation Algorithm Based on Attention Mechanism and Transfer Learning

In this paper, we propose a semantic segmentation algorithm (RoadNet) for auxiliary edge detection tasks with an attention mechanism. RoadNet improves the dispersion of the low-level features of the network model and further enhances the performance and applicability of the semantic segmentation algorithm. In RoadNet, a fully convolutional neural network is used as the basic model, an auxiliary loss in the image classification, multitask learning in machine learning, and attention mechanism in natural language processing. To improve the generalization of the model, we select and analyze a proper domain difference measure. Subsequently, the context semantic distribution module and the annotation distribution loss are designed based on the context semantic encoding structure. The domain discriminator based on the adversarial training and the adversarial training algorithm based on transfer learning are then well integrated to provide a transfer learning-based semantic segmentation algorithm (TransRoadNet). The experimental results indicate that the proposed TransRoadNet and RoadNet overperform their equivalent comparison models