Image Segmentation Algorithm Based on Attention Mechanism and Jump Connection

With the development of deep learning, convolutional neural networks have become the mainstream of computer vision algorithms. In recent years, the biggest problem of applying convolutional neural networks to image segmentation is that they cannot achieve accurate segmentation of the last layer, also cause resolution loss when extracting features, and cannot meet the demand of different pixels requiring different context dependencies. To address these issues, we add an attention mechanism and a jump feature fusion method to deeplabv3+ so that features are extracted without severe feature loss and a broader range of contextual information can be encoded into local features. The feature map is further enriched by adding a module combining bilinear upsampling and deconvolution in the process of feature restoration. Compared to previous algorithms, the results of this algorithm are superior. A performance of 85.73% is achieved on PASCAL VOC2012 using the proposed model.