SpringerLink
Log in

Autonomous vehicles’ object detection architectures ranking based on multi-criteria decision-making techniques

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Autonomous vehicles rely on object detection for safety, and various algorithms have been developed to improve speed and precision. This paper ranks 44 object detection algorithms published between 2019–2022 using COPRAS and VIKOR ranking techniques. 24 algorithms were built using LiDAR point clouds, with Convolutional Neural Networks (CNN) being the foundation. Second + MD3D, CenterPoint + MD3D, and 3D Point Cloud Object Detection Algorithm are the top three algorithms for 3D object detection using Copra’s methodologies. FlexiNet, YOLOv2-5D, and CSA-SS are the best 2D object detection algorithms. These methods are suitable for autonomous driving systems, as accuracy and speed are essential requirements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Sukanya C, Gokul R, Paul V (2016) A survey on object recognition methods. Int J Sci Eng Comput Technol 6(1):48

    Google Scholar 

  2. Rajendran R, Murugan B (2023) A novel model for eliminating overlap** issues in 3D object recognition using dove swarm optimization based light GBM. Int J Inf Technol 15(5):2387–2393

    Google Scholar 

  3. Wang Y et al (2021) Towards a physical-world adversarial patch for blinding object detection models. Inf Sci 556:459–471

    MathSciNet  Google Scholar 

  4. Olveres J et al (2021) What is new in computer vision and artificial intelligence in medical image analysis applications. Quant Imaging Med Surg 11(8):3830

    Google Scholar 

  5. Yasin JN et al (2021) Low-cost ultrasonic based object detection and collision avoidance method for autonomous robots. Int J Inf Technol 13:97–107

    Google Scholar 

  6. Kumar A et al (2022) Vision-based outdoor navigation of self-driving car using lane detection. Int J Inf Technol 14(1):215–227

    MathSciNet  Google Scholar 

  7. Xu X et al (2020) BANet: a balanced atrous net improved from SSD for autonomous driving in smart transportation. IEEE Sens J 21(22):25018–25026

    Google Scholar 

  8. Zhang S et al (2023) E-detector: asynchronous spatio-temporal for event-based object detection in intelligent transportation system. ACM Trans Multimed Comput Commun Appl 20(2):1–20

    Google Scholar 

  9. Yuan T et al (2022) Machine learning for next-generation intelligent transportation systems: a survey. Trans Emerg Telecommun Technol 33(4):e4427

    Google Scholar 

  10. Feng D et al (2020) Deep multi-modal object detection and semantic segmentation for autonomous driving: datasets, methods, and challenges. IEEE Trans Intell Transp Syst 22(3):1341–1360

    Google Scholar 

  11. Teli TA, Wani MA (2021) A fuzzy based local minima avoidance path planning in autonomous robots. Int J Inf Technol 13:33–40

    Google Scholar 

  12. Khan A et al (2020) Complex environment perception and positioning based visual information retrieval. Int J Inf Technol 12:409–417

    Google Scholar 

  13. Ye Y et al (2020) Sarpnet: shape attention regional proposal network for lidar-based 3D object detection. Neurocomputing 379:53–63

    Google Scholar 

  14. Qi CR et al (2017) Pointnet++: deep hierarchical feature learning on point sets in a metric space. Adv Neural Inf Process Syst 30:1–10

    Google Scholar 

  15. Scarselli F et al (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80

    Google Scholar 

  16. Xu B, Chen Z (2018) Multi-level fusion based 3D object detection from monocular images. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE/CVF, Salt Lake City, UT, USA

  17. Mousavian A et al (2017) 3D bounding box estimation using deep learning and geometry. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Honolulu, HI, USA

  18. Chen X et al (2016) Monocular 3D object detection for autonomous driving. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Las Vegas, NV, USA

  19. Shi Y et al (2022) Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 471:219–229

    Google Scholar 

  20. Lin C et al (2022) CL3D: camera-LiDAR 3D object detection with point feature enhancement and point-guided fusion. IEEE Trans Intell Transp Syst 23(10):18040–18050

    Google Scholar 

  21. Liu W et al (2016) SSD: single shot multibox detector. In: European conference on computer vision. Springer, Berlin

  22. Redmon J et al (2016) You only look once: unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Las Vegas, NV, USA

  23. Girshick R et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Columbus, OH, USA

  24. Ren S et al (2015) Faster R-Cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:1–9

    MathSciNet  Google Scholar 

  25. Fernandes D et al (2021) Point-cloud based 3D object detection and classification methods for self-driving applications: a survey and taxonomy. Inf Fusion 68:161–191

    Google Scholar 

  26. Qian R, Lai X, Li X (2022) 3D object detection for autonomous driving: a survey. Pattern Recogn 130:108796

  27. Wen L-H, Jo K-H (2022) Deep learning-based perception systems for autonomous driving: a comprehensive survey. Neurocomputing 489:255–270

    Google Scholar 

  28. Guo Y et al (2020) Deep learning for 3D point clouds: a survey. IEEE Trans Pattern Anal Mach Intell 43(12):4338–4364

    Google Scholar 

  29. Arnold E et al (2019) A survey on 3D object detection methods for autonomous driving applications. IEEE Trans Intell Transp Syst 20(10):3782–3795

    Google Scholar 

  30. Zamanakos G et al (2021) A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving. Comput Graph 99:153–181

    Google Scholar 

  31. Alaba SY, Ball JE (2022) A survey on deep-learning-based lidar 3D object detection for autonomous driving. Sensors 22(24):9577

    Google Scholar 

  32. Liang W et al (2021) A survey of 3D object detection. Multimed Tools Appl 80(19):29617–29641

    Google Scholar 

  33. Kaklauskas A, Zavadskas EK, Raslanas S, Ginevicius R, Komka A, Malinauskas P (2006) Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: a lithuanian case. Energy Build 38(5):454–462. https://doi.org/10.1016/j.enbuild.2005.08.005

    Article  Google Scholar 

  34. Opricovic S, Tzeng G-H (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455. https://doi.org/10.1016/S0377-2217(03)00020-1

    Article  Google Scholar 

  35. Esmaeilzadeh A et al (2021) The use of VIKOR method to set up place locating of processing plant (case study: processing plant of south of West Azerbaijan). J Soft Comput Civ Eng 5(1):38–48

    Google Scholar 

  36. Jiang H, Lu Y, Chen S (2022) Research on 3D point cloud object detection algorithm for autonomous driving. Math Probl Eng. 2022:1–13

    Google Scholar 

  37. Choi J et al (2022) MD3D: mixture-density-based 3D object detection in point clouds. IEEE Access 10:104011–104022

    Google Scholar 

  38. Cai Y et al (2021) YOLOv4-5D: an effective and efficient object detector for autonomous driving. IEEE Trans Instrum Meas 70:1–13

    Google Scholar 

  39. Liang T et al (2022) ALODAD: an anchor-free lightweight object detector for autonomous driving. IEEE Access 10:40701–40714

    Google Scholar 

  40. Singh D, Srivastava R (2022) Channel spatial attention based single-shot object detector for autonomous vehicles. Multimed Tools Appl 81(16):22289–22305

    Google Scholar 

  41. Mehtab S, Yan WQ (2022) Flexible neural network for fast and accurate road scene perception. Multimed Tools Appl 81(5):7169–7181

    Google Scholar 

  42. Chen X et al (2022) 3D object detector: a multiscale region proposal network based on autonomous driving. Comput Electr Eng 104:108412

    Google Scholar 

  43. Wang L et al (2020) 3D object detection based on sparse convolution neural network and feature fusion for autonomous driving in smart cities. Sustain Cities Soc 54:102002. https://doi.org/10.1016/j.scs.2019.102002

    Article  Google Scholar 

  44. Wang Z et al (2021) 3D MSSD: a multilayer spatial structure 3D object detection network for mobile LiDAR point clouds. Int J Appl Earth Obs Geoinf 102:102406

    Google Scholar 

  45. Zhang J, Liu H, Lu J (2022) A semi-supervised 3D object detection method for autonomous driving. Displays 71:102117

    Google Scholar 

  46. Sun J et al (2022) Semantic-aware 3D-voxel CenterNet for point cloud object detection. Comput Electr Eng 98:107677

    Google Scholar 

  47. Tong G et al (2022) ASCNet: 3D object detection from point cloud based on adaptive spatial context features. Neurocomputing 475:89–101

    Google Scholar 

  48. Liu S et al (2022) SMS-Net: sparse multi-scale voxel feature aggregation network for LiDAR-based 3D object detection. Neurocomputing 501:555–565

    Google Scholar 

  49. Qian R, Lai X, Li X (2022) BADet: boundary-aware 3D object detection from point clouds. Pattern Recognit 125:108524

    Google Scholar 

  50. He Y et al (2021) DVFENet: dual-branch voxel feature extraction network for 3D object detection. Neurocomputing 459:201–211

    Google Scholar 

  51. Li Z et al (2022) Spatial information enhancement network for 3D object detection from point cloud. Pattern Recognit 128:108684

    Google Scholar 

  52. Wang L et al (2023) SAT-GCN: self-attention graph convolutional network-based 3D object detection for autonomous driving. Knowl Based Syst 259:110080

    Google Scholar 

  53. Li X, Zhang Y, Kong D (2022) E2-PV-RCNN: improving 3D object detection via enhancing keypoint features. Multimed Tools Appl 81(25):35843–35874

    Google Scholar 

  54. **ao W et al (2022) 3D-VDNet: exploiting the vertical distribution characteristics of point clouds for 3D object detection and augmentation. Image Vis Comput 127:104557

    Google Scholar 

  55. Loc HD, Kim G-W (2022) AEC3D: an efficient and compact single stage 3D multiobject detector for autonomous driving. IEEE Trans Intell Transp Syst 23(12):23422–23432

    Google Scholar 

  56. Du L et al (2021) Ago-net: association-guided 3D point cloud object detection network. IEEE Trans Pattern Anal Mach Intell 44(11):8097–8109

    Google Scholar 

  57. Li J et al (2021) P2V-RCNN: point to voxel feature learning for 3D object detection from point clouds. IEEE Access, 2021. 9: p. 98249-98260.

    Google Scholar 

  58. Wang G et al (2021) CenterNet3D: an anchor free object detector for point cloud. IEEE Trans Intell Transp Syst 23(8):12953–12965

    Google Scholar 

  59. Zhou S et al (2020) Deep SCNN-based real-time object detection for self-driving vehicles using lidar temporal data. IEEE Access 8:76903–76912

    Google Scholar 

  60. Shi S et al (2020) From points to parts: 3D object detection from point cloud with part-aware and part-aggregation network. IEEE Trans Pattern Anal Mach Intell 43(8):2647–2664

    MathSciNet  Google Scholar 

  61. Wang Z et al (2019) SCNet: subdivision coding network for object detection based on 3D point cloud. IEEE Access 7:120449–120462

    Google Scholar 

  62. Yuan Z et al (2021) Temporal-channel transformer for 3D lidar-based video object detection for autonomous driving. IEEE Trans Circuits Syst Video Technol 32(4):2068–2078

    Google Scholar 

  63. Chen W, Li P, Zhao H (2022) MSL3D: 3D object detection from monocular, stereo and point cloud for autonomous driving. Neurocomputing 494:23–32

    Google Scholar 

  64. Hong D-S et al (2020) CrossFusion net: deep 3D object detection based on RGB images and point clouds in autonomous driving. Image Vis Comput 100:103955

    Google Scholar 

  65. Li J et al (2022) Dual-view 3D object recognition and detection via Lidar point cloud and camera image. Robot Auton Syst 150:103999

    Google Scholar 

  66. Liang Z, Huang Y, Liu Z (2022) Efficient graph attentional network for 3D object detection from Frustum-based LiDAR point clouds. J Vis Commun Image Represent 89:103667. https://doi.org/10.1016/j.jvcir.2022.103667

    Article  Google Scholar 

  67. Lin C et al (2022) 3D-DFM: anchor-free multimodal 3-D object detection with dynamic fusion module for autonomous driving. IEEE Trans Neural Netw Learn Syst. p 1–11

  68. Zhao X et al (2020) Fusion of 3D LIDAR and camera data for object detection in autonomous vehicle applications. IEEE Sens J 20(9):4901–4913

    Google Scholar 

  69. Chen J, Bai T (2020) SAANet: spatial adaptive alignment network for object detection in automatic driving. Image Vis Comput 94:103873

    Google Scholar 

  70. Ji C, Liu G, Zhao D (2022) ETS-3D: an efficient two-stage framework for stereo 3D object detection. J Vis Commun Image Represent 88:103634

    Google Scholar 

  71. Li P, Zhao H (2021) Monocular 3D object detection using dual quadric for autonomous driving. Neurocomputing 441:151–160

    Google Scholar 

  72. Wang L, Goldluecke B (2021) Sparse-Pointnet: see further in autonomous vehicles. IEEE Robot Autom Lett 6(4):7049–7056

    Google Scholar 

  73. Tian D et al (2020) Sa-yolov3: an efficient and accurate object detector using self-attention mechanism for autonomous driving. IEEE Trans Intell Transp Syst

  74. Wei J et al (2019) Enhanced object detection with deep convolutional neural networks for advanced driving assistance. IEEE Trans Intell Transp Syst 21(4):1572–1583

    Google Scholar 

  75. Al Duhayyim M et al (2022) Design of robust deep learning-based object detection and classification model for autonomous driving applications. Soft Comput 26(16):7641–7652

    Google Scholar 

  76. Liang T et al (2022) DetectFormer: category-assisted transformer for traffic scene object detection. Sensors 22(13):4833

    Google Scholar 

  77. Wu X et al (2022) Depth dynamic center difference convolutions for monocular 3D object detection. Neurocomputing 520:73–81

    Google Scholar 

  78. Ji C, Liu G, Zhao D (2022) Monocular 3D object detection via estimation of paired keypoints for autonomous driving. Multimedia Tools Appl 81(4):5973–5988. https://doi.org/10.1007/s11042-021-11801-3

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Knowledge and Information Science, Faculty of Psychology and Education, Kharazmi University, Tehran, Iran

    Parinaz Babaei, Nosrat Riahinia & Ali Azimi

  2. Department of Information Technology Management, Kharazmi University, No. 43. South Mofatteh Ave., Tehran, Iran

    Omid Mahdi Ebadati E.

Authors
  1. Parinaz Babaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nosrat Riahinia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omid Mahdi Ebadati E.
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Azimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omid Mahdi Ebadati E..

Ethics declarations

Conflict of interest

The authors did not receive support from any organization for the submitted work.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Babaei, P., Riahinia, N., Ebadati E., O.M. et al. Autonomous vehicles’ object detection architectures ranking based on multi-criteria decision-making techniques. Int. j. inf. tecnol. 16, 2343–2352 (2024). https://doi.org/10.1007/s41870-023-01517-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-023-01517-y

Keywords

Search

Navigation