Multiscale object detection on complex architectural floor plans.

Architectural floor plans are essential documents for conveying building information among designers, engineers, and clients. Automated analysis of floor plans enhances user productivity and accuracy, though research on automatic object detection within architectural floor plans has been limited. In this paper, a convolutional neural network (CNN) based architecture, ArchNetv2, is proposed to detect various visual objects, such as stairs, windows, and doors. The proposed ArchNetv2 includes a convolutional block attention module to improve feature learning. It works at multiple detection scales and can efficiently recognize large objects (e.g., stairs) and small objects (e.g., windows) simultaneously. Experimental results show that ArchNetv2 can recognize thirteen types of objects commonly found in architectural floor plans with a mAP of 93.5%, which is superior compared to the state-of-the-art techniques. The proposed architecture can serve as an important module in an automated floor plan analysis system. • Novel technique for detecting 13 object types in a complex floor plan image. • The proposed technique is based on an efficient deep learning network. • The mean average precision for object detection is over 93%. • The technique is very fast and requires less than 100 ms for one image on a typical desktop computer. [ABSTRACT FROM AUTHOR]