Real-time semantic segmentation network based on parallel atrous convolution for short-term dense concatenate and attention feature fusion

Abstract

To address the problem of incomplete segmentation of large objects and miss-segmentation of tiny objects that is universally existing in semantic segmentation algorithms, PACAMNet, a real-time segmentation network based on short-term dense concatenate of parallel atrous convolution and fusion of attentional features is proposed, called PACAMNet. First, parallel atrous convolution is introduced to improve the short-term dense concatenate module. By adjusting the size of the atrous factor, multi-scale semantic information is obtained to ensure that the last layer of the module can also obtain rich input feature maps. Second, attention feature fusion module is proposed to align the receptive fields of deep and shallow feature maps via depth-separable convolutions with different sizes, and the channel attention mechanism is used to generate weights to effectively fuse the deep and shallow feature maps. Finally, experiments are carried out based on both Cityscapes and CamVid datasets, and the segmentation accuracy achieve 77.4% and 74.0% at the inference speeds of 98.7 FPS and 134.6 FPS, respectively. Compared with other methods, PACAMNet improves the inference speed of the model while ensuring higher segmentation accuracy, so PACAMNet achieve a better balance between segmentation accuracy and inference speed.