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Semantic segmentation has been a hot topic across diverse research fields.Along with the success of deep convolutional neural networks, semanticsegmentation has made great achievements and improvements, in terms of bothurban scene parsing and indoor semantic segmentation. However, most of thestate-of-the-art models are still faced with a challenge in discriminativefeature learning, which limits the ability of a model to detect multi-scaleobjects and to guarantee semantic consistency inside one object or distinguishdifferent adjacent objects with similar appearance. In this paper, a practicaland efficient edge-aware neural network is presented for semantic segmentation.This end-to-end trainable engine consists of a new encoder-decoder network, alarge kernel spatial pyramid pooling (LKPP) block, and an edge-aware lossfunction. The encoder-decoder network was designed as a balanced structure tonarrow the semantic and resolution gaps in multi-level feature aggregation,while the LKPP block was constructed with a densely expanding receptive fieldfor multi-scale feature extraction and fusion. Furthermore, the new powerfuledge-aware loss function is proposed to refine the boundaries directly from thesemantic segmentation prediction for more robust and discriminative features.The effectiveness of the proposed model was demonstrated using Cityscapes,CamVid, and NYUDv2 benchmark datasets. The performance of the two structuresand the edge-aware loss function in ELKPPNet was validated on the Cityscapesdataset, while the complete ELKPPNet was evaluated on the CamVid and NYUDv2datasets. A comparative analysis with the state-of-the-art methods under thesame conditions confirmed the superiority of the proposed algorithm.

ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation