Semantic segmentation of multiclass walls in complex architectural floor plan image

Automatic floor plan image analysis is becoming popular in the construction industry. An architectural floor plan provides the layout of a building floor and includes objects such as walls, doors, windows, and stairs. Detection of the walls in a floor plan image is important as the walls typically define the main layout of the floor and individual rooms. In existing literature, the walls are typically detected as a single class object. However, in construction type floor plans, the walls are represented by different drawings (e.g., solid-wall, dot-wall, diagonal-wall, hollow-wall, and gray-wall) based on the raw materials used for construction. Detection of multiclass walls would be desirable for applications such as materials cost estimation by builders and building information modeling. A convolutional neural network, namely WallNetv2, is proposed for semantic segmentation of multiclass walls in a floor plan image. WallNetv2 consists of an encoder, a channel contextual module, a spatial contextual module, and a decoder. The encoder extracts the hierarchical features from the input floor plan image. The channel and spatial contextual modules capture the relationship of the high-level features among channels and pixels, respectively. The decoder further processes the learned features and recovers the spatial information gradually with connections to the low-level features. The experimental results show that the proposed WallNetv2 achieves a mean IoU of 70%, which is superior to the state-of-the-art techniques.