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.

针对语义分割算法中普遍存在的大物体分割不完整和小物体误分割的问题，PACAMNet是一种基于并行空洞卷积短时密集级联和注意力特征融合的实时分割网络被提出，称为 PACAMNet。首先，引入并行空洞卷积来改进短期密集连接模块。通过调整atrous因子的大小，获得多尺度的语义信息，保证模块的最后一层也能获得丰富的输入特征图。其次，提出了注意力特征融合模块，通过不同大小的深度可分离卷积来对齐深浅特征图的感受野，并利用通道注意力机制生成权重来有效融合深浅特征图。最后，基于Cityscapes和CamVid数据集进行实验，在98.7 FPS和134.6 FPS的推理速度下，分割精度分别达到77.4%和74.0%。与其他方法相比，PACAMNet在保证更高的分割精度的同时提高了模型的推理速度，因此PACAMNet在分割精度和推理速度之间取得了更好的平衡。