基于深度学习的道路障碍物检测方法

doi:10.11772/j.issn.1001-9081.2019122227

计算机应用 ›› 2020, Vol. 40 ›› Issue (8): 2428-2433.DOI: 10.11772/j.issn.1001-9081.2019122227

• 应用前沿、交叉与综合 • 上一篇下一篇

基于深度学习的道路障碍物检测方法

彭育辉, 郑玮鸿, 张剑锋

福州大学机械工程及自动化学院, 福州 350116

收稿日期:2020-01-05 修回日期:2020-02-25 发布日期:2020-05-13 出版日期:2020-08-10
通讯作者: 彭育辉(1975-),男,福建莆田人,教授,博士,主要研究方向:汽车无人驾驶、计算机辅助图形图像,pengyuhui@fzu.edu.cn
作者简介:郑玮鸿(1994-),男,福建莆田人,硕士研究生,主要研究方向:点云数据处理、深度学习、三维目标检测;张剑锋(1995-),男,福建泉州人,硕士研究生,主要研究方向:同时定位与地图构建、点云数据处理。
基金资助:
福建省科技厅产学合作重大项目（2017H6007）。

Deep learning-based on-road obstacle detection method

PENG Yuhui, ZHENG Weihong, ZHANG Jianfeng

School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou Fujian 350116, China

Received:2020-01-05 Revised:2020-02-25 Online:2020-05-13 Published:2020-08-10
Supported by:
This work is partially supported by Fujian Science and Technology Department Major Program of Industry-University Collaboration (2017H6007).

摘要/Abstract

摘要： 针对基于激光雷达（LiDAR）的三维点云数据处理及道路障碍物检测的问题，提出一种基于深度学习的路障碍物检测方法。首先，采用统计滤波算法对原始点云进行离群点的剔除处理；其次，提出一种端到端的深度神经网络VNMax，利用最大池化对区域候选网络（RPN）架构进行优化，构建改进的目标检测层；最后，在KITTI数据集上进行了训练及测试实验。结果显示，经过滤波处理，点云中各点之间的平均距离得到有效减少。通过对在KITTI数据集的简单、中等和困难任务的车辆定位处理结果比较得出，所提方法的平均精度比VoxelNet（Unofficial）分别提高了11.3个百分点、6.02个百分点和3.89个百分点。实验测试结果表明，统计滤波算法仍是有效的三维点云数据处理手段，最大池化模块可以提高深度神经网络的学习性能和目标定位能力。

关键词: 无人驾驶, 深度学习, 激光雷达, 目标检测, 三维点云

Abstract: Concerning the problems of 3D point cloud data processing and on-road obstacle detection based on Light Detection And Ranging (LiDAR), a deep learning-based on-road obstacle detection method was proposed. First, the statistical filtering algorithm was applied to eliminate the outliers from the original point cloud, improving the roughness of point clouds. Then, an end-to-end deep neural network named VNMax was proposed, the max pooling was used to optimize the structure of Region Proposal Network (RPN), and an improved target detection layer was built. Finally, training and testing experiments were performed on KITTI dataset. The results show that, by filtering, the average distance between the points in point cloud is reduced effectively. For the car location processing results of easy, medium difficult and hard detection tasks in KITTI dataset, it can be seen that the average precisions of the proposed method are improved by 11.30 percentage points, 6.02 percentage points and 3.89 percentage points, respectively, compared with those of the VoxelNet. Experimental results show that the statistical filtering algorithm is still an effective 3D point cloud data processing method, and the max pooling module can improve the learning performance and object location ability of the deep neural network.

Key words: autonomous driving, deep learning, Light Detection And Ranging (LiDAR), object detection, 3D-point cloud

中图分类号:

TN958.98

彭育辉, 郑玮鸿, 张剑锋. 基于深度学习的道路障碍物检测方法[J]. 计算机应用, 2020, 40(8): 2428-2433.

PENG Yuhui, ZHENG Weihong, ZHANG Jianfeng. Deep learning-based on-road obstacle detection method[J]. Journal of Computer Applications, 2020, 40(8): 2428-2433.

参考文献

[1] GEIGER A, LENZ P, URTASUN R. Are we ready for autonomous driving? the KITTI vision benchmark suite[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2012:3354-3361.
[2] WU Z, SONG S, KHOSLA A, et al. 3D ShapeNets:a deep representation for volumetric shapes[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2015:1912-1920.
[3] SONG S, XIAO J. Sliding shapes for 3D object detection in depth images[C]//Proceedings of the 13th European Conference on Computer Vision, LNCS 8694. Cham:Springer, 2014:634-651
[4] MATURANA D, SCHERER S. VoxNet:a 3D convolutional neural network for real-time object recognition[C]//Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE, 2015:922-928.
[5] SONG S, XIAO J. Deep sliding shapes for amodal 3D object detection in RGB-D images[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:808-816.
[6] ZHOU Y, TUZEL O. VoxelNet:end-to-end learning for point cloud based 3D object detection[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2018:4490-4499.
[7] 曾钰廷. 基于深度学习的物体检测与跟踪方法的研究[D]. 南昌:东华理工大学, 2018:59-65. (ZENG Y T. Object detection and tracking based on the deep learning[D]. Nanchang:East Chian University of Technology, 2018:59-65.)
[8] YAN Y, MAO Y, LI B. SECOND:sparsely embedded convolutional detection[J]. Sensors, 2018, 18(10):3337.
[9] UY M A, LEE G H. PointNetVLAD:deep point cloud based retrieval for large-scale place recognition[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2018:4470-4479.
[10] DENG H, BIRDAL T, ILIC S. PPFNet:global context aware local features for robust 3D point matching[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2018:195-205.
[11] QI C R, SU H, MO K, et al. PointNet:deep learning on point sets for 3D classification and segmentation[C]//Proceedings of the 2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2017:77-85.
[12] 王胜文,张彬,孙菁聪. PointNet的点云数据集的破损测试与深度解读[J]. 中国传媒大学学报(自然科学版), 2019, 26(3):51-57. (WANG S W, ZHANG B, SUN J C. Breaking test and deep interpretation of PointNet's point cloud dataset[J]. Journal of Communication University of China (Science and Technology), 2019, 26(3):51-57.)
[13] 赵中阳,程英蕾,释小松,等. 基于多尺度特征和PointNet的LiDAR点云地物分类方法[J]. 激光与光电子学进展, 2019, 56(5):243-250. (ZHAO Z Y, CHENG Y L, SHI X S, et al. Terrain classification of LiDAR point cloud based on multi-scale features and PointNet[J]. Laser and Optoelectronics Progress, 2019, 56(5):243-250.)
[14] QI C R, YI L, SU H, et al. PointNet++:deep hierarchical feature learning on point sets in a metric space[C]//Proceedings of the 31st Annual Conference on Neural Information Processing Systems. Red Hook:Curran Associates Inc., 2017:5100-5109.
[15] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:unified, real-time object detection[C]//Proceedings of the 2016 IEEE conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:779-788
[16] REDMON J, FARHADI A. YOLO9000:better, faster, stronger[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2017:6517-6525.
[17] SIMON M, MILZ S, AMENDE K, et al. Complex-YOLO:an Euler-region-proposal for real-time 3D object detection on point clouds[C]//Proceedings of the 2018 European Conference on Computer Vision, LNCS 11129. Cham:Springer, 2018:197-209.
[18] ALI W, ABDELKARIM S, ZIDAN M, et al. YOLO3D:end-to-end real-time 3D oriented object bounding box detection from LiDAR point cloud[C]//Proceedings of the 2018 European Conference on Computer Vision, LNCS 11131. Cham:Springer, 2018:716-728.
[19] 王林,张鹤鹤. Faster R-CNN模型在车辆检测中的应用[J]. 计算机应用, 2018, 38(3):666-670. (WANG L, ZHANG H H. Application of Faster R-CNN model in vehicle detection[J]. Journal of Computer Applications, 2018, 38(3):666-670.)
[20] LIANG M, YANG B, WANG S, et al. Deep continuous fusion for multi-sensor 3D object detection[C]//Proceedings of the 15th European Conference on Computer Vision, LNCS 11220. Cham:Springer, 2018:663-678.
[21] DU X, ANG M H, KARAMAN S, et al. A general pipeline for 3D detection of vehicles[C]//Proceedings of the 2018 IEEE International Conference on Robotics and Automation. Piscataway:IEEE, 2018:3194-3200.
[22] KU J, MOZIFIAN M, LEE J, et al. Joint 3D proposal generation and object detection from view aggregation[C]//Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE, 2018:1-8.
[23] QI C R, LIU W, WU C, et al. Frustum PointNets for 3D object detection from RGB-D data[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2018:918-927.
[24] CHEN X, MA H, WAN J, et al. Multi-view 3D object detection network for autonomous driving[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2017:6526-6534.
[25] 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, 2017, 39(6):1137-1149.
[26] LI, B, ZHANG T, XIA T. Vehicle detection from 3D lidar using fully convolutional network[EB/OL].[2019-12-21].http://www.roboticsproceedings.org/rss12/p42.pdf.

基于深度学习的道路障碍物检测方法

Deep learning-based on-road obstacle detection method

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	秦璟, 秦志光, 李发礼, 彭悦恒. 基于概率稀疏自注意力神经网络的重性抑郁疾患诊断[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2970-2974.
[2]	王熙源, 张战成, 徐少康, 张宝成, 罗晓清, 胡伏原. 面向手术导航3D/2D配准的无监督跨域迁移网络[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2911-2918.
[3]	黄云川, 江永全, 黄骏涛, 杨燕. 基于元图同构网络的分子毒性预测[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2964-2969.
[4]	李顺勇, 李师毅, 胥瑞, 赵兴旺. 基于自注意力融合的不完整多视图聚类算法[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2696-2703.
[5]	潘烨新, 杨哲. 基于多级特征双向融合的小目标检测优化模型[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2871-2877.
[6]	张英俊, 李牛牛, 谢斌红, 张睿, 陆望东. 课程学习指导下的半监督目标检测框架[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2326-2333.
[7]	刘禹含, 吉根林, 张红苹. 基于骨架图与混合注意力的视频行人异常检测方法[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2551-2557.
[8]	顾焰杰, 张英俊, 刘晓倩, 周围, 孙威. 基于时空多图融合的交通流量预测[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2618-2625.
[9]	石乾宏, 杨燕, 江永全, 欧阳小草, 范武波, 陈强, 姜涛, 李媛. 面向空气质量预测的多粒度突变拟合网络[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2643-2650.
[10]	李烨恒, 罗光圣, 苏前敏. 基于改进YOLOv5的Logo检测算法[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2580-2587.
[11]	赵亦群, 张志禹, 董雪. 基于密集残差物理信息神经网络的各向异性旅行时计算方法[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2310-2318.
[12]	徐松, 张文博, 王一帆. 基于时空信息的轻量视频显著性目标检测网络[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2192-2199.
[13]	孙逊, 冯睿锋, 陈彦如. 基于深度与实例分割融合的单目3D目标检测方法[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2208-2215.
[14]	吴筝, 程志友, 汪真天, 汪传建, 王胜, 许辉. 基于深度学习的患者麻醉复苏过程中的头部运动幅度分类方法[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2258-2263.
[15]	姬张建, 杜娜. 基于改进VariFocalNet的微小目标检测[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2200-2207.