Open Access iconOpen Access

ARTICLE

crossmark

Intelligent Risk-Identification Algorithm with Vision and 3D LiDAR Patterns at Damaged Buildings

Dahyeon Kim1, Jiyoung Min1, Yongwoo Song1, Chulsu Kim2, Junho Ahn1,*

1 Computer Information Technology, Korea National University of Transportation, Chungju, 27469, Korea
2 Department of Railway Vehicle System Engineering, Korea National University of Transportation, Uiwang, 16106, Korea

* Corresponding Author: Junho Ahn. Email: email

Intelligent Automation & Soft Computing 2023, 36(2), 2315-2331. https://doi.org/10.32604/iasc.2023.034394

Abstract

Existing firefighting robots are focused on simple storage or fire suppression outside buildings rather than detection or recognition. Utilizing a large number of robots using expensive equipment is challenging. This study aims to increase the efficiency of search and rescue operations and the safety of firefighters by detecting and identifying the disaster site by recognizing collapsed areas, obstacles, and rescuers on-site. A fusion algorithm combining a camera and three-dimension light detection and ranging (3D LiDAR) is proposed to detect and localize the interiors of disaster sites. The algorithm detects obstacles by analyzing floor segmentation and edge patterns using a mask regional convolutional neural network (mask R-CNN) features model based on the visual data collected from a parallelly connected camera and 3D LiDAR. People as objects are detected using you only look once version 4 (YOLOv4) in the image data to localize persons requiring rescue. The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm and estimate the distance to the actual object using the center point of the clustering result. The proposed artificial intelligence (AI) algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detect floor surfaces, atypical obstacles, and persons requiring rescue. Accordingly, the fused AI algorithm was comparatively verified.

Keywords


Cite This Article

APA Style
Kim, D., Min, J., Song, Y., Kim, C., Ahn, J. (2023). Intelligent risk-identification algorithm with vision and 3D lidar patterns at damaged buildings. Intelligent Automation & Soft Computing, 36(2), 2315-2331. https://doi.org/10.32604/iasc.2023.034394
Vancouver Style
Kim D, Min J, Song Y, Kim C, Ahn J. Intelligent risk-identification algorithm with vision and 3D lidar patterns at damaged buildings. Intell Automat Soft Comput . 2023;36(2):2315-2331 https://doi.org/10.32604/iasc.2023.034394
IEEE Style
D. Kim, J. Min, Y. Song, C. Kim, and J. Ahn "Intelligent Risk-Identification Algorithm with Vision and 3D LiDAR Patterns at Damaged Buildings," Intell. Automat. Soft Comput. , vol. 36, no. 2, pp. 2315-2331. 2023. https://doi.org/10.32604/iasc.2023.034394



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 839

    View

  • 405

    Download

  • 0

    Like

Share Link