Computer Science

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).

PDF (PC)

813

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
[1] ZHU Q.Three-dimensional GIS and its Application in SmartCity[J].Geo-Information Science,2014,16(2):151-157.
[2] LI S L,XIE W J,LI L,et al.A review of computer rapid building modeling methods[J].Journal of Geographical Sciences,2019,42(9):1966-1990.
[3] AGARWAL S,FURUKAWA Y,SNAVELY N,et al.Building Rome in a day[J].Communicationsof the ACM,2011,54(10):105-112.
[4] FURUKAWA Y,PONCE J.Accurate,dense,and robust multi-view stereopsis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,32(8):1362-1376.
[5] WU C,AGARWAL S,CURLESS B,et al.Multicore bundle adjustment[C]//Proceedings of Computer Vision and Pattern Recognition.IEEE,2011:3057-3064.
[6] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich featurehierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2014:580-587.
[7] GIRSHICK R.Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1440-1448.
[8] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,39(6):1137-1149.
[9] HE K,GKIOXARI G,DOLLÁR P,et al.Mask r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2961-2969.
[10] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:779-788.
[11] REDMON J,FARHADI A.YOLO9000:better,faster,stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:7263-7271.
[12] REDMON J,FARHADI A.Yolov3:An incremental improvement[J].arXiv:1804.02767,2018.
[13] LIU W,ANGUELOV D,ERHAN D,et al.Ssd:Single shotmultibox detector[C]//Proceedings of European Conference on Computer Vision.Cham:Springer,2016:21-37.
[14] CHEN J,ZHANG Y Q,SONG P,et al.Application of DeepLearning in 3D Reconstruction of Objects Based on Single Image[J].IEEE/CAA Journal of Automatica Sinica (JAS),2019,45(4):657-668.
[15] TEKIN B,SINHA S N,FUA P.Real-time seamless single shot 6d object pose prediction[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition (CVPR).2018:292-301.
[16] LI B,OUYANG W,SHENG L,et al.Gs3d:An efficient 3dobject detection framework for autonomous driving[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:1019-1028.
[17] MOUSAVIAN A,ANGUELOV D,FLYNN J,et al.3d boun-ding box estimation using deep learning and geometry[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2017:7074-7082.
[18] LI P,CHEN X,SHEN S.Stereo r-cnn based 3d object detection for autonomous driving[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:7644-7652.
[19] QI C R,LIU W,WU C,et al.Frustum pointnets for 3d object detection from rgb-d data[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition (CVPR).2018:918-927.
[20] SHI S,WANG X,LI H.Pointrcnn:3d object proposal generation and detection from point cloud[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2019:770-779.
[21] SHI S,WANG Z,WANG X,et al.Part-a2 net:3d part-aware and aggregation neural network for object detection from point cloud[J].arXiv:1907.03670,2019.
[22] KUANG H,WANG B,AN J,et al.Voxel-FPN:Multi-ScaleVoxel Feature Aggregation for 3D Object Detection from LIDAR Point Clouds[J/OL].https://arxiv.org/abs/1907.05286.
[23] LIU Z,GAO G,SUN L,et al.IPG-Net:Image Pyramid Guidance Network for Small Object Detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPR).2020:1026-1027.
[1] CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75.
[2] LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao. Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients [J]. Computer Science, 2022, 49(9): 183-193.
[3] LI Bin, WAN Yuan. Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment [J]. Computer Science, 2022, 49(8): 86-96.
[4] LIU Dong-mei, XU Yang, WU Ze-bin, LIU Qian, SONG Bin, WEI Zhi-hui. Incremental Object Detection Method Based on Border Distance Measurement [J]. Computer Science, 2022, 49(8): 136-142.
[5] WANG Can, LIU Yong-jian, XIE Qing, MA Yan-chun. Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization [J]. Computer Science, 2022, 49(8): 157-164.
[6] CHEN Yong-ping, ZHU Jian-qing, XIE Yi, WU Han-xiao, ZENG Huan-qiang. Real-time Helmet Detection Algorithm Based on Circumcircle Radius Difference Loss [J]. Computer Science, 2022, 49(6A): 424-428.
[7] YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng. SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion [J]. Computer Science, 2022, 49(6A): 256-260.
[8] MAO Sen-lin, XIA Zhen, GENG Xin-yu, CHEN Jian-hui, JIANG Hong-xia. FCM Algorithm Based on Density Sensitive Distance and Fuzzy Partition [J]. Computer Science, 2022, 49(6A): 285-290.
[9] CHEN Jing-nian. Acceleration of SVM for Multi-class Classification [J]. Computer Science, 2022, 49(6A): 297-300.
[10] Ran WANG, Jiang-tian NIE, Yang ZHANG, Kun ZHU. Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids [J]. Computer Science, 2022, 49(6): 44-54.
[11] XING Yun-bing, LONG Guang-yu, HU Chun-yu, HU Li-sha. Human Activity Recognition Method Based on Class Increment SVM [J]. Computer Science, 2022, 49(5): 78-83.
[12] ZHU Zhe-qing, GENG Hai-jun, QIAN Yu-hua. Line-Segment Clustering Algorithm for Chemical Structure [J]. Computer Science, 2022, 49(5): 113-119.
[13] ZHANG Yu-jiao, HUANG Rui, ZHANG Fu-quan, SUI Dong, ZHANG Hu. Study on Affinity Propagation Clustering Algorithm Based on Bacterial Flora Optimization [J]. Computer Science, 2022, 49(5): 165-169.
[14] HU Fu-yuan, WAN Xin-jun, SHEN Ming-fei, XU Jiang-lang, YAO Rui, TAO Zhong-ben. Survey Progress on Image Instance Segmentation Methods of Deep Convolutional Neural Network [J]. Computer Science, 2022, 49(5): 10-24.
[15] XU Tao, CHEN Yi-ren, LYU Zong-lei. Study on Reflective Vest Detection for Apron Workers Based on Improved YOLOv3 Algorithm [J]. Computer Science, 2022, 49(4): 239-246.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.