Quantitative Evaluations of Deep Learning Models for Rapid Building Damage Detection in Disaster Areas

Junho Ser, Byungyun Yang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

Original languageEnglish
Pages (from-to)381-391
Number of pages11
JournalJournal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
Volume40
Issue number5
DOIs
StatePublished - 2022

Keywords

  • Damaged Detection
  • Deep Learning Model
  • Disaster Management
  • One-Stage Detector
  • Very High-Resolution Satellite Image

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