| Title |
Performance Analysis of Temporary Structure Recognition Using YOLOv8 and YOLOv11 Segmentation Models |
| Authors |
우윤희(Woo, Yun-Hee) ; 유무영(Yoo, Moo-Young) |
| DOI |
https://doi.org/10.5659/JAIK.2025.41.10.339 |
| Keywords |
Temporary Structures; Object Detection; Segmentation Performance; Smart Annotation |
| Abstract |
This study evaluates the detection performance of YOLOv8 and YOLOv11 segmentation models for temporary structures in construction
images. The dataset consists of labeled images featuring scaffolding, formwork, and other temporary components commonly found on
construction sites. Both models were trained and tested under the same conditions, and their performance was compared using mean Average
Precision (mAP) and Frames Per Second (FPS) as evaluation metrics. To analyze the impact of labeling quality on detection results, two
annotation methods?manual polygon and smart polygon?were applied. The experimental results showed that the YOLOv8s model achieved
the most balanced performance between accuracy and speed, making it suitable for real-time applications. In addition, smart polygon
annotations enhanced boundary precision and improved detection accuracy, particularly for complex-shaped structures. This study demonstrates
the practical applicability of segmentation-based object detection models in construction environments and provides insight into the
development of intelligent visual monitoring systems for automation and quality control in the field. |