Small Object Detection in 3D Urban Scenes

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.

Keywords

• 3d urban model|multi-view|small objects|object detection|clustering