FloorYOLO: an attention-enhanced YOLOv8n for floor plan object recognition

The precise and automatic recognition and classification of structural elements like doors, walls, and windows, as well as categories of rooms in floor plan images, is crucial to the present-day application of real estate and architecture. Traditional approaches have the problems that the spatial relation is missing, the intra-class variance is high, dense packing is common, and clearly the boundary is hard to define, which has led to the development of efficient deep learning-based detection architectures.

In this paper, a novel approach to automatic detection and localization of structural components on floor plans based on YOLOv8 with an Efficient Channel Attention (ECA) module is introduced. In this technique, YOLOv8 acts as the backbone model for detecting multiple classes, while the ECA block improves the representation of features by considering local interactions among channels without imposing significant extra computation. Moreover, the PAN neck enhances multilevel features with fine spatial information as well as semantic information in the image data, and the YOLOv8 head performs the detection task. The proposed system was tested using the SESYD dataset and the CVC-FP dataset, which include dense arrangements and scale variations in images.

Our model demonstrates improved discrimination between visually similar intraclass symbol variants that have proven difficult for both rule-based and deep learning approaches, with the proposed model achieving mAP@0.5:0.95 of 0.9794 and mAP@0.5 of 0.9950 on the 16-class SESYD benchmark, and mAP@0.5 of 0.8241 on CVC-FP against the baseline's 0.8116.

The proposed FloorYOLO approach represents one of the first dual-dataset evaluations across synthetic and real-world floor plan benchmarks, demonstrating that lightweight channel attention can be effectively integrated into existing detection backbones to improve structural element recognition in floor plan imagery.