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.