Detection of Components in Korean Apartment Complexes Using Instance Segmentation

Abstract

Since the 2000s, the demand for enhancing the quality of life of Korean apartment complexes has led to the development of units with diverse outdoor spaces. Analyzing these complexes requires detailed layout data, which are challenging to obtain from construction drawings. This study addresses this issue using the Roboflow map API to collect data based on apartment complex addresses. The dataset, categorized into seven classes, trained a YOLOv8s-seg segmentation model, which was evaluated by precision, recall, and mAP values per class. Detection performance was generally high, although vehicle roads and welfare facilities posed challenges. Segmenting complexes, analyzing main building layouts, and classifying based on period, household count, and regional shape are potential applications. This study is significant because it secured a dataset of layout drawings through maps, a challenging feat given the difficulty in obtaining actual completion blueprints of apartment complexes. However, discrepancies existed between the mapped layouts and the actual blueprints, which caused certain errors; this represents a limitation of the study. Nevertheless, the apartment complex layout analysis model derived from this study is expected to be useful for various future research projects. We anticipate that further studies will be able to conduct architectural planning research on apartment complexes based on an improved analysis model.

Keywords

• deep learning
• YOLOv8
• image segmentation
• apartment complex