Computer Science ›› 2022, Vol. 49 ›› Issue (6): 238-244.doi: 10.11896/jsjkx.210400174

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Small Object Detection in 3D Urban Scenes

CHEN Jia-zhou1, ZHAO Yi-bo1, XU Yang-hui1, MA Ji1, JIN Ling-feng1,2, QIN Xu-jia1   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310012,China
    2 Digital Space Technology R&D Center,Southeast Digital Economic Development Institute,Quzhou,Zhejiang 324000,China
  • Received:2021-04-17 Revised:2021-08-09 Online:2022-06-15 Published:2022-06-08
  • About author:CHEN Jia-zhou,born in 1984,Ph.D,associate professor,master supervisor,is a member of China Computer Federation.His main research interests include computer graphics and visual analysis.
    MA Ji,born in 1985,Ph.D,lecturer,is a member of China Computer Federation.His main research interests include data visualization and so on.
  • Supported by:
    Science and Technology Protection Project of Cultural Relics in Zhejiang Province(2020014),National Natural Science Foundation of China(61902350) and Science and Technology Project of Quzhou(2019K38).

Abstract: 3D object detection is the core of semantic analysis in 3D urban scenes,but the existing object detection methods mainly focus on large objects such as buildings and roads,while the detection accuracy of these methods for small objects such as street lamps and manhole covers is low.For this sake,a multi-view small object detection method for 3D urban scenes is proposed.It combines the oblique photogrammetry and 3D object localization,to improve the detection accuracy of small objects.Firstly,small objects are detected in the UAV images using a deep neural network.Then,detection results are back projected onto the three-dimensional urban model.Finally,the 3D detection results are obtained by clustering these 3D objects obtained by back projection.Experimental results show that the proposed method can automatically detect small objects such as manhole covers and windows on the large-scale 3D urban model reconstructedby oblique photogrammetry,it is free of spatial occlusion,and has high accuracy and stability compared with object detection on orthophoto maps.

Key words: 3D urban model, Clustering, Multi-view, Object detection, Small objects

CLC Number: 

  • TP391
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