DAPSPNet: Deep Aggregation Pyramid Strip Pooling Network for Real-time and Accurate Segmentation

This paper introduces an efficient Convolutional Neural Networks (CNN) architecture named DAPSPNet for Real-time semantic segmentation. We propose a novel dual-resolution network, DAPSPNet, and augment it with strip pooling in the multi-scale feature extraction module to extract strip-shaped features more effectively. The convolution kernels have lengths of 5, 9, and 17, with a width of 1. We chose strip pooling as the supplement for two reasons. First, strip pooling is a lightweight technique that reduces the number of parameters and computations involved in pooling operations. Second, there are also some strip-shaped features in the contextual information, which are in line with the needs of real-time semantic segmentation of road scenes. Extensive experimental evaluations on the Cityscapes dataset demonstrate the competitive performance of DAPSPNet compared to several state-of-the-art methods in most scenarios.