Semantic segmentation is a crucial operation in the computer vision field. One of the promising techniques is the convolutional neural network (CNN). It can be utilized with both single and multidimensional arrays and is useful for processing 2D arrays in computer vision tasks. In this paper, a new model for semantic scene segmentation is proposed. In order to enhance the segmentation results, the model starts with classifying the input scene as either indoor or outdoor scenes. In this context, the MobileNet is used as it provides better results when compared to Inception-v3 and Inception-ResNet-v2 networks. The next step, two models based on Pyramid Scene Parsing Network (PSPNet) are used for image segmentation (indoor images are segmented by the indoor model and outdoor images are segmented by the outdoor model). Experimental results prove the concept that a specific scene model can achieve higher accuracy than general scene models on the semantic segmentation task.

语义分割是计算机视觉领域中的关键操作。卷积神经网络（CNN）是有前途的技术之一。它可以与一维和多维数组一起使用，对于在计算机视觉任务中处理2D数组很有用。本文提出了一种新的语义场景分割模型。为了增强分割结果，模型首先将输入场景分类为室内或室外场景。在这种情况下，使用MobileNet是因为与Inception-v3和Inception-ResNet-v2网络相比，它提供了更好的结果。下一步，使用基于金字塔场景解析网络（PSPNet）的两个模型进行图像分割（室内模型由室内模型分割，室外图像由室外模型分割）。