计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 238-244.doi: 10.11896/jsjkx.210400174
陈佳舟1, 赵熠波1, 徐阳辉1, 马骥1, 金灵枫1,2, 秦绪佳1
CHEN Jia-zhou1, ZHAO Yi-bo1, XU Yang-hui1, MA Ji1, JIN Ling-feng1,2, QIN Xu-jia1
摘要： 三维目标检测是三维城市场景语义分析的关键环节,但是现有的目标检测方法主要关注诸如建筑、道路等较大的物体,对路灯、井盖等小物体的检测误差较大。为此,提出了一种多视图的三维城市场景小物体检测方法,在倾斜摄影的基础上结合精准三维定位方法,提高了三维城市场景中小物体检测的精度。首先在无人机原片上利用深度学习方法检测城市小物体,然后将这些图像检测结果反投影到三维城市模型上,并通过聚类得到最终的三维检测结果。实验结果表明,所提方法能够在倾斜摄影测量得到的大规模三维城市模型上自动检测井盖、窗户等城市小物体,不受视线遮挡的影响,相对于正射图上的物体检测具有较高的准确性和稳定性。
中图分类号:
| [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. |
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