[1]陆军,李杨,鲁林超.远距离和遮挡下三维目标检测算法研究[J].智能系统学报,2024,19(2):259-266.[doi:10.11992/tis.202301001]
LU Jun,LI Yang,LU Linchao.Long-distance and occluded 3D target detection algorithm[J].CAAI Transactions on Intelligent Systems,2024,19(2):259-266.[doi:10.11992/tis.202301001]
点击复制
LU Jun,LI Yang,LU Linchao.Long-distance and occluded 3D target detection algorithm[J].CAAI Transactions on Intelligent Systems,2024,19(2):259-266.[doi:10.11992/tis.202301001]
远距离和遮挡下三维目标检测算法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第2期
页码:
259-266
栏目:
学术论文—机器学习
出版日期:
2024-03-05
- Title:
- Long-distance and occluded 3D target detection algorithm
- Keywords:
- target detection; deep learning; Lidar point cloud; long-distance target; occluded target; autopilot; regional pyramid; feature extraction
- 分类号:
- TP391.41
- 文献标志码:
- 2023-11-22
- 摘要:
- 针对现有三维目标检测算法对存在遮挡及距离较远目标检测效果差的问题，以基于点云的三维目标检测算法(3D object proposal generation and detection from point cloud, PointRCNN)为基础，对网络进行改进，提高三维目标检测精度。对区域生成网络(region proposal network, RPN)获取的提议区域(region of interest, ROI)体素化处理，同时构建不同尺度的区域金字塔来捕获更加广泛的兴趣点；加入点云 Transformer 模块来增强对网格中心点局部特征的学习；在网络中加入球查询半径预测模块，使得模型可以根据点云密度自适应调整球查询的范围。最后，对所提算法的有效性进行了试验验证，在 KITTI 数据集下对模型的性能进行评估测试，同时设计相应的消融试验验证模型中各模块的有效性。
- Abstract:
- To address the limitations of existing 3D target detection algorithms, particularly their poor detection performance for occluded and long-distance objects, we have implemented an enhancement to the PointRCNN network, a 3D object detection algorithm based on point cloud. We began by voxelizing the region of interest obtained from the region proposal network and constructing region pyramids of different scales to capture a wider range of points of interest. Simultaneously, we introduced a point cloud transformer module to enhance the learning of the local features of grid center points. Moreover, we incorporated a sphere query radius prediction module into the network. This addition allows the model to adaptively adjust the sphere query range according to the density of the point cloud. Finally, the effectiveness of the proposed algorithm was validated through rigorous experimental testing. We evaluated the performance of the model using the KITTI data set and designed corresponding ablation experiments to verify the effectiveness of each module in the model.
- 参考文献/References:
-
[1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84–90.
[2] 张驰, 郭媛, 黎明. 人工神经网络模型发展及应用综述[J]. 计算机工程与应用, 2021, 57(11): 57–69
ZHANG Chi, GUO Yuan, LI Ming. Review of development and application of artificial neural network models[J]. Computer engineering and applications, 2021, 57(11): 57–69
[3] 许德刚, 王露, 李凡. 深度学习的典型目标检测算法研究综述[J]. 计算机工程与应用, 2021, 57(8): 10–25
XU Degang, WANG Lu, LI Fan. Review of typical object detection algorithms for deep learning[J]. Computer engineering and applications, 2021, 57(8): 10–25
[4] GUO Menghao, XU Tianxing, LIU Jiangjiang, et al. Attention mechanisms in computer vision: A survey[J]. Computational visual media, 2022(3): 331–368.
[5] ZHANG Suzhi, WU Yuhong, CHANG Jun. Survey of image recognition algorithms[C]//2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference. Chongqing: IEEE, 2020: 542-548.
[6] YUAN Xiaohui, SHI Jianfang, GU Lichuan. A review of deep learning methods for semantic segmentation of remote sensing imagery[J]. Expert systems with applications, 2021, 169: 114417.
[7] FANG Wei, SHEN Liang, CHEN Yupeng. Survey on image object detection algorithms based on deep learning[C]//SUN X, ZHANG X, XIA Z, et al. International Conference on Artificial Intelligence and Security. Cham: Springer, 2021: 468-480.
[8] UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al. Selective search for object recognition[J]. International journal of computer vision, 2013, 104(2): 154–171.
[9] GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2016: 1440-1448.
[10] REN Shaoqing, HE Kaiming, 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, 2017, 39(6): 1137–1149.
[11] HE Kaiming, GKIOXARI G, DOLL?R P, et al. Mask R-CNN[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence. [S. l. ]: IEEE, 2018: 386-397.
[12] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
[13] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788.
[14] XIA C, WEI P, WEI W, et al. A multilevel fusion network for 3D object detection[J]. Neurocomputing, 2021, 437(2): 107–117.
[15] WEN Lihua, JO K H. Three-attention mechanisms for one-stage 3-D object detection based on LiDAR and camera[J]. IEEE transactions on industrial informatics, 2021, 17(10): 6655–6663.
[16] SHI Shaoshuai, GUO Chaoxu, JIANG Li, et al. PV-RCNN: point-voxel feature set abstraction for 3D object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 10526-10535.
[17] YIN Tianwei, ZHOU Xingyi, KR?HENB?HL P. Center-based 3D object detection and tracking[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 11779-11788.
[18] ZHOU Yin, TUZEL O. VoxelNet: end-to-end learning for point cloud based 3D object detection[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 4490-4499.
[19] LANG A H, VORA S, CAESAR H, et al. PointPillars: fast encoders for object detection from point clouds[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2020: 12689-12697.
[20] LI Y, BU R, SUN M, et al. Point CNN: Convolution On X-Transformed Points[EB/OL]. (2018-01-23)[2023-10-15]. https://arxiv.org/abs/1801.07791.
[21] LI Yusheng, TIAN Yong, TIAN Jiandong. Sparse 3D point clouds segmentation considering 2D image feature extraction with deep learning[C]//Proc SPIE 11179, Eleventh International Conference on Digital Image Processing. Guangzhou: SPIE, 2019, 11179: 62-70.
[22] TANG Xu, ZHANG Huayu, MA Jingjing, et al. Supervised adaptive-RPN network for object detection in remote sensing images[C]//IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa: IEEE, 2021: 2647-2650.
[23] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30: 6000–6010.
[24] GEIGER A, LENZ P, STILLER C, et al. Vision meets robotics: the KITTI dataset[J]. The international journal of robotics research, 2013, 32(11): 1231–1237.
[25] 李亚超, 熊德意, 张民. 神经机器翻译综述[J]. 计算机学报, 2018, 41(12): 2734–2755
LI Yachao, XIONG Deyi, ZHANG Min. A survey of neural machine translation[J]. Chinese journal of computers, 2018, 41(12): 2734–2755
[26] GU Fengwei, LU Jun, CAI Chengtao, et al. EANTrack: an efficient attention network for visual tracking[J]. IEEE transactions on automation science and engineering, 2023(99): 1–18.
[27] GU Fengwei, LU Jun, CAI Chengtao. RPformer: a robust parallel transformer for visual tracking in complex scenes[J]. IEEE transactions on instrumentation and measurement, 2022, 71: 1–14.
[28] 石磊, 王毅, 成颖, 等. 自然语言处理中的注意力机制研究综述[J]. 数据分析与知识发现, 2020, 4(5): 1–14
SHI Lei, WANG Yi, CHENG Ying, et al. Review of attention mechanism in natural language processing[J]. Data analysis and knowledge discovery, 2020, 4(5): 1–14
[29] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 298-300.
[30] AGARAP A F. Deep learning using rectified linear units (ReLU)[EB/OL]. (2018-04-22)[2023-10-15]. https://arxiv.org/abs/1803.08375.
[31] QI C R, YI Li, SU Hao, et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[J]. Advances in neural information processing systems, 2017, 30: 30–38.
- 相似文献/References:
-
[1]胡光龙,秦世引.动态成像条件下基于SURF和Mean shift的运动目标高精度检测[J].智能系统学报,2012,7(1):61.
HU Guanglong,QIN Shiyin.High precision detection of a mobile object under dynamic imaging based on SURF and Mean shift[J].CAAI Transactions on Intelligent Systems,2012,7():61.
[2]韩峥,刘华平,黄文炳,等.基于Kinect的机械臂目标抓取[J].智能系统学报,2013,8(2):149.[doi:10.3969/j.issn.1673-4785.201212038]
HAN Zheng,LIU Huaping,HUANG Wenbing,et al.Kinect-based object grasping by manipulator[J].CAAI Transactions on Intelligent Systems,2013,8():149.[doi:10.3969/j.issn.1673-4785.201212038]
[3]张媛媛,霍静,杨婉琪,等.深度信念网络的二代身份证异构人脸核实算法[J].智能系统学报,2015,10(2):193.[doi:10.3969/j.issn.1673-4785.201405060]
ZHANG Yuanyuan,HUO Jing,YANG Wanqi,et al.A deep belief network-based heterogeneous face verification method for the second-generation identity card[J].CAAI Transactions on Intelligent Systems,2015,10():193.[doi:10.3969/j.issn.1673-4785.201405060]
[4]丁科,谭营.GPU通用计算及其在计算智能领域的应用[J].智能系统学报,2015,10(1):1.[doi:10.3969/j.issn.1673-4785.201403072]
DING Ke,TAN Ying.A review on general purpose computing on GPUs and its applications in computational intelligence[J].CAAI Transactions on Intelligent Systems,2015,10():1.[doi:10.3969/j.issn.1673-4785.201403072]
[5]韩延彬,郭晓鹏,魏延文,等.RGB和HSI颜色空间的一种改进的阴影消除算法[J].智能系统学报,2015,10(5):769.[doi:10.11992/tis.201410010]
HAN Yanbin,GUO Xiaopeng,WEI Yanwen,et al.An improved shadow removal algorithm based on RGB and HSI color spaces[J].CAAI Transactions on Intelligent Systems,2015,10():769.[doi:10.11992/tis.201410010]
[6]曾宪华,易荣辉,何姗姗.流形排序的交互式图像分割[J].智能系统学报,2016,11(1):117.[doi:10.11992/tis.201505037]
ZENG Xianhua,YI Ronghui,HE Shanshan.Interactive image segmentation based on manifold ranking[J].CAAI Transactions on Intelligent Systems,2016,11():117.[doi:10.11992/tis.201505037]
[7]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报,2016,11(3):279.[doi:10.11992/tis.201603026]
MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11():279.[doi:10.11992/tis.201603026]
[8]刘帅师,程曦,郭文燕,等.深度学习方法研究新进展[J].智能系统学报,2016,11(5):567.[doi:10.11992/tis.201511028]
LIU Shuaishi,CHENG Xi,GUO Wenyan,et al.Progress report on new research in deep learning[J].CAAI Transactions on Intelligent Systems,2016,11():567.[doi:10.11992/tis.201511028]
[9]马世龙,乌尼日其其格,李小平.大数据与深度学习综述[J].智能系统学报,2016,11(6):728.[doi:10.11992/tis.201611021]
MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11():728.[doi:10.11992/tis.201611021]
[10]王亚杰,邱虹坤,吴燕燕,等.计算机博弈的研究与发展[J].智能系统学报,2016,11(6):788.[doi:10.11992/tis.201609006]
WANG Yajie,QIU Hongkun,WU Yanyan,et al.Research and development of computer games[J].CAAI Transactions on Intelligent Systems,2016,11():788.[doi:10.11992/tis.201609006]
[11]葛园园,许有疆,赵帅,等.自动驾驶场景下小且密集的交通标志检测[J].智能系统学报,2018,13(3):366.[doi:10.11992/tis.201706040]
GE Yuanyuan,XU Youjiang,ZHAO Shuai,et al.Detection of small and dense traffic signs in self-driving scenarios[J].CAAI Transactions on Intelligent Systems,2018,13():366.[doi:10.11992/tis.201706040]
[12]莫宏伟,汪海波.基于Faster R-CNN的人体行为检测研究[J].智能系统学报,2018,13(6):967.[doi:10.11992/tis.201801025]
MO Hongwei,WANG Haibo.Research on human behavior detection based on Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2018,13():967.[doi:10.11992/tis.201801025]
[13]刘召,张黎明,耿美晓,等.基于改进的Faster R-CNN高压线缆目标检测方法[J].智能系统学报,2019,14(4):627.[doi:10.11992/tis.201905026]
LIU Zhao,ZHANG Liming,GENG Meixiao,et al.Object detection of high-voltage cable based on improved Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2019,14():627.[doi:10.11992/tis.201905026]
[14]单义,杨金福,武随烁,等.基于跳跃连接金字塔模型的小目标检测[J].智能系统学报,2019,14(6):1144.[doi:10.11992/tis.201905041]
SHAN Yi,YANG Jinfu,WU Suishuo,et al.Skip feature pyramid network with a global receptive field for small object detection[J].CAAI Transactions on Intelligent Systems,2019,14():1144.[doi:10.11992/tis.201905041]
[15]赵振兵,江爱雪,戚银城,等.嵌入遮挡关系模块的SSD模型的输电线路图像金具检测[J].智能系统学报,2020,15(4):656.[doi:10.11992/tis.202001008]
ZHAO Zhenbing,JIANG Aixue,QI Yincheng,et al.Fittings detection in transmission line images with SSD model embedded occlusion relation module[J].CAAI Transactions on Intelligent Systems,2020,15():656.[doi:10.11992/tis.202001008]
[16]张新钰,邹镇洪,李志伟,等.面向自动驾驶目标检测的深度多模态融合技术[J].智能系统学报,2020,15(4):758.[doi:10.11992/tis.202002010]
ZHANG Xinyu,ZOU Zhenhong,LI Zhiwei,et al.Deep multi-modal fusion in object detection for autonomous driving[J].CAAI Transactions on Intelligent Systems,2020,15():758.[doi:10.11992/tis.202002010]
[17]王照国,张红云,苗夺谦.基于F1值的非极大值抑制阈值自动选取方法[J].智能系统学报,2020,15(5):1006.[doi:10.11992/tis.202006056]
WANG Zhaoguo,ZHANG Hongyun,MIAO Duoqian.Automatic selection method of non-maximum suppression threshold based on F1 score[J].CAAI Transactions on Intelligent Systems,2020,15():1006.[doi:10.11992/tis.202006056]
[18]伍锡如,凌星雨.基于改进的Faster RCNN面部表情检测算法[J].智能系统学报,2021,16(2):210.[doi:10.11992/tis.201910020]
WU Xiru,LING Xingyu.Facial expression recognition based on improved Faster RCNN[J].CAAI Transactions on Intelligent Systems,2021,16():210.[doi:10.11992/tis.201910020]
[19]翟永杰,杨旭,赵振兵,等.融合共现推理的Faster R-CNN输电线路金具检测[J].智能系统学报,2021,16(2):237.[doi:10.11992/tis.202012023]
ZHAI Yongjie,YANG Xu,ZHAO Zhenbing,et al.Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection[J].CAAI Transactions on Intelligent Systems,2021,16():237.[doi:10.11992/tis.202012023]
[20]洪恺临,曹江涛,姬晓飞.改进Center-Net网络的自主喷涂机器人室内窗户检测[J].智能系统学报,2021,16(3):425.[doi:10.11992/tis.202005016]
HONG Kailin,CAO Jiangtao,JI Xiaofei.Indoor window detection of autonomous spraying robot based on improved CenterNet network[J].CAAI Transactions on Intelligent Systems,2021,16():425.[doi:10.11992/tis.202005016]
备注/Memo
收稿日期:2023-01-02。
基金项目:国家自然科学基金项目（52171332）；黑龙江省自然科学基金项目（F201123）.
作者简介:陆军，教授，博士生导师，主要研究方向为计算机视觉、智能控制技术和机械臂控制。获国防科学技术进步奖一等奖3项，省部级二等奖1项，省部级三等奖3项，授权发明专利10项。发表学术论文70余篇。E-mail：lujun0260@sina.com;李杨，硕士研究生，主要研究方向为点云目标检测、目标跟踪，机器视觉、图像处理。E-mail：liyang142857@126.com;鲁林超，硕士研究生，主要研究方向为点云目标检测、特征融合，图像处理。E-mail：llczsr@163.com
通讯作者:陆军. E-mail：lujun0260@sina.com
更新日期/Last Update:
1900-01-01