Bilateral U‐Net semantic segmentation with spatial attention mechanism

Abstract Aiming at the problem that the existing models have a poor segmentation effect on imbalanced data sets with small‐scale samples, a bilateral U‐Net network model with a spatial attention mechanism is designed. The model uses the lightweight MobileNetV2 as the backbone network for feature hierarchical extraction and proposes an Attentive Pyramid Spatial Attention (APSA) module compared to the Attenuated Spatial Pyramid module, which can increase the receptive field and enhance the information, and finally adds the context fusion prediction branch that fuses high‐semantic and low‐semantic prediction results, and the model effectively improves the segmentation accuracy of small data sets. The experimental results on the CamVid data set show that compared with some existing semantic segmentation networks, the algorithm has a better segmentation effect and segmentation accuracy, and its mIOU reaches 75.85%. Moreover, to verify the generality of the model and the effectiveness of the APSA module, experiments were conducted on the VOC 2012 data set, and the APSA module improved mIOU by about 12.2%.