The recognition of information in floor plan data requires the use of detection and segmentation models. However, relying on several single-task models can result in ineffective utilization of relevant information when there are multiple tasks present simultaneously. To address this challenge, we introduce MuraNet, an attention-based multi-task model for segmentation and detection tasks in floor plan data. In MuraNet, we adopt a unified encoder called MURA as the backbone with two separated branches: an enhanced segmentation decoder branch and a decoupled detection head branch based on YOLOX, for segmentation and detection tasks respectively. The architecture of MuraNet is designed to leverage the fact that walls, doors, and windows usually constitute the primary structure of a floor plan's architecture. By jointly training the model on both detection and segmentation tasks, we believe MuraNet can effectively extract and utilize relevant features for both tasks. Our experiments on the CubiCasa5k public dataset show that MuraNet improves convergence speed during training compared to single-task models like U-Net and YOLOv3. Moreover, we observe improvements in the average AP and IoU in detection and segmentation tasks, respectively.Our ablation experiments demonstrate that the attention-based unified backbone of MuraNet achieves better feature extraction in floor plan recognition tasks, and the use of decoupled multi-head branches for different tasks further improves model performance. We believe that our proposed MuraNet model can address the disadvantages of single-task models and improve the accuracy and efficiency of floor plan data recognition.

翻译：户型图数据中的信息识别需要依赖检测与分割模型。然而，当同时存在多个任务时，依赖多个单任务模型可能导致相关信息利用效率低下。为解决这一挑战，我们提出了MuraNet——一种基于注意力的多任务模型，用于户型图数据中的分割与检测任务。在MuraNet中，我们采用名为MURA的统一编码器作为主干网络，并配备两个独立分支：一个增强型分割解码器分支，以及一个基于YOLOX的解耦检测头分支，分别用于分割与检测任务。MuraNet的架构设计充分利用了墙体、门窗通常构成户型图主要结构这一特性。通过联合训练检测与分割任务，我们相信MuraNet能够有效提取并利用两个任务的相关特征。在CubiCasa5k公开数据集上的实验表明，相较于U-Net和YOLOv3等单任务模型，MuraNet在训练过程中收敛速度更快。此外，我们观察到检测任务的平均AP与分割任务的平均IoU均有所提升。消融实验证明，MuraNet基于注意力的统一主干网络在户型图识别任务中实现了更优的特征提取，而针对不同任务采用解耦多头分支进一步提升了模型性能。我们相信，所提出的MuraNet模型能够克服单任务模型的不足，提高户型图数据识别的准确性与效率。