Indoor layout estimation is one of the important research topics in the field of computer vision, and it is widely used in three-dimensional reconstruction, robot navigation, and virtual reality. The current indoor layout estimation solutions have problems such as poor real-time performance and large calculations. To deal with these problems, this paper proposes a lightweight convolutional network based on multi-task supervision. The network model is based on the encoder-decoder structure and uses indoor edge heatmaps and planar semantic segmentation to achieve multi-task supervised learning. In addition, this paper modifies the convolution module, replaced 1×3 and 3×1 convolution with 1×1 convolution, which improves the real-time performance of the network while ensuring the accuracy of the model. The experimental results conducted on the public dataset LSUN show that the proposed method has good real-time performance and accuracy.