计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (11): 133-147.doi: 10.11896/jsjkx.231000075

• 计算机图形学&多媒体 • 上一篇    下一篇

自动驾驶场景下的图像三维目标检测研究进展

周燕1,2, 许业文1, 蒲磊1, 徐雪妙2, 刘翔宇1, 周月霞1   

  1. 1 佛山大学电子信息工程学院 广东 佛山 528000
    2 华南理工大学计算机科学与工程学院 广州 510641
  • 收稿日期:2023-10-12 修回日期:2024-03-28 出版日期:2024-11-15 发布日期:2024-11-06
  • 通讯作者: 许业文(2112203032@stu.fosu.edu.cn)
  • 作者简介:(zhouyan791266@fosu.edu.cn)
  • 基金资助:
    国家自然科学基金(61972091);广东省自然科学基金(2022A1515010101,2021A1515012639);广东省普通高校重点研究项目(2020ZDZX3049);佛山市科技创新项目(2020001003285);广东省教育科学规划课题(2021GXJK445)

Research Progress of Image 3D Object Detection in Autonomous Driving Scenario

ZHOU Yan1,2, XU Yewen1, PU Lei1, XU Xuemiao2, LIU Xiangyu1, ZHOU Yuexia1   

  1. 1 School of Electronic Information Engineering,Foshan University,Foshan,Guangdong 528000,China
    2 School of Computer Science and Engineering,South China University of Technology,Guangzhou 510641,China
  • Received:2023-10-12 Revised:2024-03-28 Online:2024-11-15 Published:2024-11-06
  • About author:ZHOU Yan,born in 1979,master,professor,master supervisor,is a member of CCF(No.60294M).Her main research interests include machine vision,gra-phics and image processing,and its applications in public safety,intelligent manufacturing and so on.
    XU Yewen,born in 1999,postgraduate.His main research interests include computer vision and 3D object detection.
  • Supported by:
    National Natural Science Foundation of China(61972091),Natural Science Foundation of Guangdong Province,China(2022A1515010101,2021A1515012639),Key Research Project of Universities of Guangdong Province(2020ZDZX3049),Science and Technology Innovation Project of FoShan(2020001003285) and Educational Science Planning Project of Guangdong Province,China(2021GXJK445).

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摘要： 二维目标检测技术由于缺乏对物理世界尺寸、深度等信息的描述,在自动驾驶场景中应用还存在较大的局限性。许多研究者结合自动驾驶实际需要,在图像三维目标检测上做了许多探索。为了对该领域进行全面研究,文中对近年来国内外发表的相关文献进行综述,介绍了基于图像的三维目标检测以及图像与点云融合的三维目标检测两类方法,并根据网络对输入数据的不同处理方式,对两类方法进一步细分,阐述了各个类别中的代表性方法,对各类方法的优劣进行总结,对比并分析了各算法的性能。此外,详细介绍了自动驾驶场景下三维目标检测的相关数据集和评价指标。最后,对图像三维目标检测领域中存在的挑战和困难进行了分析,并对未来可能的研究方向进行了展望。

关键词: 图像三维目标检测, 深度学习, 自动驾驶, 多模态融合, 计算机视觉

Abstract: 2D object detection techniques have significant limitations when applied to automatic driving scenarios due to the absence of description of the size,depth and other information of the physical environment.Numerous researchers have made extensive explorations in the field of image 3D object detection by aligning with the practical requirements of automatic driving.To conduct a comprehensive study in this domain,this paper reviews recent literature published both domestically and internationally.It introduces two main categories of methods:image-based 3D object detection and 3D object detection by fusing image and point cloud data.Furthermore,it further subdivides these categories based on the different approaches used to process input data by the network.The paper describes representative methods within each category,summarizes the strengths and weaknesses of each method,and conducts a comparative analysis of their performance.Additionally,it provides a detailed introduction to relevant datasets and evaluation metrics for 3D object detection in autonomous driving scenarios.Finally,the paper analyzes the challenges and difficulties in the field of image 3D object detection,and outlines potential future research directions.

Key words: Image 3D object detection, Deep learning, Automatic driving, Multimodal fusion, Computer vision

中图分类号: 

  • TP391
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