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Deep Convolutional Neural Networks (DCNNs) have recently shown outstandingperformance in semantic image segmentation. However, state-of-the-artDCNN-based semantic segmentation methods usually suffer from high computationalcomplexity due to the use of complex network architectures. This greatly limitstheir applications in the real-world scenarios that require real-timeprocessing. In this paper, we propose a real-time high-performance DCNN-basedmethod for robust semantic segmentation of urban street scenes, which achievesa good trade-off between accuracy and speed. Specifically, a LightweightBaseline Network with Atrous convolution and Attention (LBN-AA) is firstly usedas our baseline network to efficiently obtain dense feature maps. Then, theDistinctive Atrous Spatial Pyramid Pooling (DASPP), which exploits thedifferent sizes of pooling operations to encode the rich and distinctivesemantic information, is developed to detect objects at multiple scales.Meanwhile, a Spatial detail-Preserving Network (SPN) with shallow convolutionallayers is designed to generate high-resolution feature maps preserving thedetailed spatial information. Finally, a simple but practical Feature FusionNetwork (FFN) is used to effectively combine both shallow and deep featuresfrom the semantic branch (DASPP) and the spatial branch (SPN), respectively.Extensive experimental results show that the proposed method respectivelyachieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU)with the inference speed of 51.0 fps and 39.3 fps on the challenging Cityscapesand CamVid test datasets (by only using a single NVIDIA TITAN X card). Thisdemonstrates that the proposed method offers excellent performance at thereal-time speed for semantic segmentation of urban street scenes.

Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes