提出了一种基于深度学习的语义分割网络。该网络通过多孔卷积设计了一个能提取图像多尺度信息的空间金字塔模块,并通过大量实验探索了空间金字塔模块中多孔采样率和多尺度分支对于网络场景解析能力的影响。讨论了网络训练中不同超参数对于网络性能的影响。在SUN RGB-D数据集上的测试结果显示,与其它state-of-the-art的语义分割网络相比,本文设计的网络性能突出。最后,还对基于红外图像的语义分割进行了初步探索。

A semantic segmentation network based on deep learning is proposed. The network designs a spatial pyramid module which can extract multi-scale information from images through Atrous convolution. It also explores the influence of Atrous convolution sampling rate and multi-scale branches on the performance of network through extensive experiment. the impact of hyperparameters on network performance during training is discussed. The test results on the SUN RGB-D dataset show that compared with other state-of-the-art semantic segmentation networks, the performance of the network we proposed is outstanding. Finally, the semantic segmentation based on infrared images is explored preliminarily.