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Vision-based Recognition Algorithm for Up-To-Date Indoor Digital Map Generations at Damaged Buildings

Computers, Materials & Continua 2022年第8期72卷 2765-2781页

作者： Dahyeon kim chulsu kim Junho Ahn Computer Information Technology Korea National University of TransportationChungju27469Korea Department of Railway Vehicle System Engineering Korea National University of TransportationUiwang16106Korea

When firefighters are engaged in search and rescue missions inside a building at a risk of collapse,they have difficulty in field command and rescue because they can only simplymonitor the situation inside the building utilizing old building drawings or *** propose an efficient solution for fast search and rescue work of firefighters,this study investigates the generation of up-to-date digital maps for disaster sites by tracking the collapse situation,and identifying the information of obstacles which are risk factors,using an artificial intelligence algorithm based on low-cost *** research separates the floor by using the mask regional convolutional neural network(R-CNN)algorithm,and determines whether the passage is collapsed or ***,in the case of a passage that can be searched,the floor pattern of the obstacles that exist on the floor that has not collapsed is analyzed,and obstacles are searched utilizing an image processing ***,we can detect various unknown as well as known ***,the locations of obstacles can be estimated using the pixel values up to the bounding box of an existing detected *** conduct experiments using the public datasets collected by Carnegie Mellon university(CMU)and data collected by manipulating a low-cost robot equipped with a smartphone while roaming five buildings in a *** collected data have various floor patterns for objectivity and obstacles that are different from one *** on these data,the algorithm for detecting unknown obstacles of a verified study and estimating their sizes had an accuracy of 93%,and the algorithm for estimating the distance to obstacles had an error rate of *** this process,we tracked collapsed passages and composed up-to-date digital maps for disaster sites that include the information of obstacles that interfere with the search and rescue work.

关键词： Vision artificial intelligence robot map generation damaged building

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

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Intelligent Automation & Soft Computing 2023年第5期36卷 2315-2331页

作者： Dahyeon kim Jiyoung Min Yongwoo Song chulsu kim Junho Ahn Computer Information Technology Korea National University of TransportationChungju27469Korea Department of Railway Vehicle System Engineering Korea National University of TransportationUiwang16106Korea

Existingﬁreﬁghting robots are focused on simple storage orﬁre sup-pression outside buildings rather than detection or *** a large number of robots using expensive equipment is *** study aims to increase the efﬁciency of search and rescue operations and the safety ofﬁreﬁgh-ters 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 loca-lize the interiors of disaster *** algorithm detects obstacles by analyzingﬂoor 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 *** as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring *** 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 *** proposed artiﬁcial intelligence(AI)algorithm was veriﬁed based on individual sensors using a sensor-mounted robot in an actual building to detectﬂoor surfaces,atypical obstacles,and persons requiring ***,the fused AI algorithm was comparatively veriﬁed.

关键词： Three-dimension light detection and ranging vision risk identiﬁcation damaged building robot

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