Skip to main content
Springer Nature Link
Log in

Progress in multi-object detection models: a comprehensive survey

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Deep learning-based object detection has become popular due to its strong learning ability and advantages in dealing with occlusion, scale transformation, and context changes. In recent years, it has become a research hotspot. This paper presents the current Deep Learning models from Generic and Salient detection models ranging from one-stage to two-stage for multi-object detection in various applications. Nevertheless, we also examined the advantages and some drawbacks of those models. Furthermore, challenges such as variation in object scales, computation time, illumination differing from various applications, and promising research directions of Deep Learning models are discussed. Finally, we proposed Dense PRediction Simplified (DPRS) based on the YOLO model. Backbones play a vital role in enhancing the performance of detection models, and efficient Backbone architecture will be fused to achieve the competitive state-of-art result.

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
$34.99 /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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of data and material (data transparency)

The data & code generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Ahmed I, Din S, Jeon G, Piccialli F (2019) Exploring deep learning models for overhead view multiple object detection. IEEE Internet Things J 7(7):5737–5744

    Article  Google Scholar 

  2. Ammirato P, Berg AC (2019) A mask-rcnn baseline for probabilistic object detection. arXiv preprint arXiv:1908.03621

  3. Aslam A Irtaza A, Nida N (2020) Object Detection and Localization in Natural Scenes Through Single-Step and Two-Step Models. In: 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), pp. 1–7. IEEE

  4. Bochkovskiy A, Wang C-Y, and Hong-Yuan ML (2004) YOLOv4: Optimal Speed and Accuracy of Object Detection. 2020. arXiv preprint arXiv:2004.10934

  5. Cai Z, Vasconcelos N (2019) Cascade R-CNN: high quality object detection and instance segmentation. IEEE Trans Pattern Anal Mach Intell

  6. Cai Z, Fan Q, Feris RS, Vasconcelos N (2016) A unified multi-scale deep convolutional neural network for fast object detection. In: European conference on computer vision, pp. 354–370. Springer, Cham

  7. Chen C, Seff A, Kornhauser AL, Xiao J (2015) Deepdriving: learning affordance for direct perception in autonomous driving, in ICCV

  8. Chen X, Ma H, Wan J, Li B, Xia T (2017) Multi-view 3D object detection network for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, HI, the USA, pp 6526–6534

  9. Chen X, Ma H, Wan J, Li B, Xia T (2017) Multi-view 3d object detection network for autonomous driving, in CVPR

  10. Christ PF, Kaissis G, Ettlinger F, Kaissis G (2017) SurvivalNet: predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks. In: Proceedings of the IEEE international conference on international symposium on biomedical imaging, Melbourne, Australia, pp. 839–843

  11. Croitoru I, Bogolin S-V, Leordeanu M (2017) Unsupervised learning from video to detect foreground objects in single images. In: ICCV

  12. Dai J, Li Y, He K, Sun J (2016) R-fcn: Object detection via region-based fully convolutional networks. arXiv preprint arXiv:1605.06409

  13. Dixit KG, Shreyas M, Chadaga G, Savalgimath SS, Ragavendra Rakshith G, Naveen Kumar MR (2019) Evaluation and evolution of object detection techniques YOLO and R-CNN. Int J Recent Technol Eng 8(3):824–829

    Google Scholar 

  14. Dollár P, Appel R, Belongie S, Perona P (2014) Fast feature pyramids for object detection. IEEE Trans Pattern Anal Mach Intell 36(8):1532–1545

    Article  Google Scholar 

  15. Dong C (2015) Chen change Loy, Kaiming He, and Xiaoou Tang. "image super-resolution using deep convolutional networks.". IEEE Trans Pattern Anal Mach Intell 38(2):295–307

    Article  Google Scholar 

  16. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2009) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  17. Foley D, O’reilly R (2018) An Evaluation of Convolutional Neural Network Models for Object Detection in Images on Low-End Devices. AICS 2259:1–12

    Google Scholar 

  18. Fu C-Y, Liu W, Ranga A, Tyagi A, Berg AC (2017) Dssd: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659

  19. Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp. 1440–1448

  20. Grauman K, Darrell T (2005) The pyramid match kernel: Discriminative classification with sets of image features. In: Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, vol. 2, pp. 1458–1465. IEEE

  21. Hanchinamani SR, Sarkar S, Bhairannawar SS (2016) Design and implementation of high-speed background subtraction algorithm for moving object detection. In: Proceedings of the IEEE international conference on advances in computing, communications and informatics, Jaipur, India, 21–24 September 2016, pp 367–374

  22. He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916

    Article  Google Scholar 

  23. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778

  24. He K, Zhang X, Ren S, Sun J (2016 ) Deep residual learning for image recognition. In: CVPR

  25. Hossain S, Lee D-j (2019) Deep learning-based real-time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices. Sensors 19(15):3371

    Article  Google Scholar 

  26. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia, pp. 675–678

  27. Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Rong Q (2019) A survey of deep learning-based object detection. IEEE Access 7:128837–128868

    Article  Google Scholar 

  28. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 25:1097–1105

    Google Scholar 

  29. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), vol. 2, pp. 2169–2178. IEEE

  30. Li Y, Li J, Lin W, Li J (2018) Tiny-DSOD: Lightweight object detection for resource-restricted usages. arXiv preprint arXiv:1807.11013

  31. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision, pp. 21–37. Springer, Cham

  32. Liu Y, Wang Y, Wang S, Liang TT, Zhao Q, Tang Z, Ling H (2020) Cbnet: a novel composite backbone network architecture for object detection. Proc AAAI Conf Art Intell 34(07):11653–11660

    Google Scholar 

  33. Long ZHOU, Wei S, Zhongma CUI, Jiaqi FANG, Xiaoting YANG, Wei DING (2020) Lira-YOLO: a lightweight model for ship detection in radar images. J Syst Eng Electron 99:1–7

    Google Scholar 

  34. Lowe G (2004) Sift-the scale invariant feature transform. Int J 2(91–110):2

    Google Scholar 

  35. Ma L, Yu L, Zhang X, Ye Y, Yin G, Johnson BA (2019) Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J Photogramm Remote Sens 152:166–177

    Article  Google Scholar 

  36. Ma B, Li X, Xia Y, Zhang Y (2020) Autonomous deep learning: a genetic DCNN designer for image classification. Neurocomputing 379:152–161

    Article  Google Scholar 

  37. Malamas EN (2003) Euripides GM Petrakis, Michalis Zervakis, Laurent petit, and Jean-Didier Legat. "a survey on industrial vision systems, applications and tools.". Image Vis Comput 21(2):171–188

    Article  Google Scholar 

  38. Mao J, Xiao T, Jiang Y, Cao Z (2017) What can help pedestrian detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Hawaii, HI, the USA, pp 3127–3136

  39. Mauri A, Khemmar R, Decoux B, Ragot N, Rossi R, Trabelsi R, Boutteau R, Ertaud J-Y, Savatier X (2020) Deep learning for real-time 3D multi-object detection, localisation, and tracking: application to smart mobility. Sensors 20(2):532

    Article  Google Scholar 

  40. Maximilian F, Liu Y, Engstle Armin, and Schneider Stefan-Alexander (2019) Deep learning-based multi-scale multi-object detection and classification for autonomous driving. In: Fahrerassistenzsysteme 2018, pp. 233–242. Springer Vieweg, Wiesbaden

  41. Mhalla A, Chateau T, Amara NEB (2019) Spatio-temporal object detection by deep learning: video-interlacing to improve multi-object tracking. Image Vis Comput 88:120–131

    Article  Google Scholar 

  42. Murthy CB, Hashmi MF, Bokde ND, Geem ZW (2020) Investigations of object detection in images/videos using various deep learning techniques and embedded platforms—a comprehensive review. Appl Sci 10(9):3280

    Article  Google Scholar 

  43. Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: European conference on computer vision, pp. 483–499. Springer, Cham

  44. Pal SK, Pramanik A, Maiti J, Mitra P (2021) Deep learning in multi-object detection and tracking: state of the art. Appl Intell 51(9):6400–6429

    Article  Google Scholar 

  45. Pathak AR, Pandey M, Rautaray S (2018) Application of deep learning for object detection. Procedia Comput Sci 132:1706–1717

    Article  Google Scholar 

  46. Poeppel D (2012) The maps problem and the mapping problem: two challenges for a cognitive neuroscience of speech and language. Cognitive Neuropsychol 29(1–2):34–55

    Article  Google Scholar 

  47. Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263–7271

  48. Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767

  49. Redmon J, Santosh D, Ross G, Ali F (2016) You only look once: Unified, real-time object detection. In: Pro-ceedings of the IEEE conference on computer vision and pattern recognition, pp. 779–788

  50. Ren S, He K, Girshick R, Sun J (2016) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  Google Scholar 

  51. Senicic M, Matijevic M, Nikitovic M (2018) Teaching the methods of object detection by robot vision”. In Proceedings of the IEEE International Convention on Information and Communication Technology, Electronics and Microelectronics, Opatija, Croatia, pp. 558–563

  52. Shaikh SH, Khalid S, Nabendu C (2014) Moving object detection approaches, challenges and object tracking. In: Moving object detection using background subtraction, pp. 5–14. Springer, Cham

  53. Shen Z, Liu Z, Li J, Jiang Y-G, Chen Y, Xue X (2017) Dsod: learning deeply supervised object detectors from scratch. In: Proceedings of the IEEE international conference on computer vision, pp 1919-1927

  54. Sreenu G, Durai M (2019) Intelligent video surveillance: A review through deep learning techniques for crowd analysis. J Big Data 6:48–75

    Article  Google Scholar 

  55. Sung KK, Poggio T (2002) Example-based learning for view-based human face detection. IEEE Trans Pattern Anal Mach Intell 20(1):39–51

    Article  Google Scholar 

  56. Timofte, Radu, De Smet V, Luc Van G (2013) Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE international conference on computer vision, pp. 1920–1927

  57. Uijlings JRR, Koen Van De Sande EA, Gevers T, Smeulders AWM (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171

    Article  Google Scholar 

  58. Wang C, Ren W, Huang K, Tan T (2014) Weakly supervised object localization with latent category learning. In: ECCV

  59. Wang C-Y, Liao H-YM, Wu Y-H, Chen P-Y, Hsieh J-W, Yeh I-H (2020) CSPNet: A new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp. 390–391

  60. Weimer D, Scholz-Reiter B, Shpitalni M (2016) Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann 65:417–420

    Article  Google Scholar 

  61. Wu J (2018) Complexity and accuracy analysis of common artificial neural networks on pedestrian detection. In: MATEC Web of Conferences232. p. 01003. EDP Sciences

  62. Wu X, Sahoo D, Hoi SCH (2020) Recent advances in deep learning for object detection. Neurocomputing 396:39–64

    Article  Google Scholar 

  63. Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw image patches. In: 2008 IEEE conference on computer vision and pattern recognition, pp. 1–8. IEEE

  64. Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873

    Article  MathSciNet  MATH  Google Scholar 

  65. Yu X, Choi W, Lin Y, Savarese S (2017) Subcategory-aware convolutional neural networks for object proposals and detection. In: 2017 IEEE winter conference on applications of computer vision (WACV), pp. 924–933. IEEE

  66. Zeiler, Matthew D., and Rob Fergus (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision, pp. 818–833. Springer, Cham

  67. Zhao L, Li S (2020) Object detection algorithm based on improved YOLOv3. Electronics 9(3):537

    Article  Google Scholar 

  68. Zhao Z, Zheng P, Xu S, Wu X (2019) Object Detection with Deep Learning: A Review. IEEE Trans Neural Netw Learn Syst 30:3212–3232

    Article  Google Scholar 

  69. Zhao Z-Q, Zheng P, Xu S-t, Xindong W (2019) Object detection with deep learning: a review. IEEE Transact Neural Net Learning Syst 30(11):3212–3232

    Article  Google Scholar 

  70. Zhou X, Gong W, Fu W, Du F (2017) Application of deep learning in object detection. In: Proceedings of the IEEE/ACIS 16th international conference on computer and information science, Wuhan, China, pp 631–634

  71. Zitnick C (2014) Lawrence, and Piotr Dollár. Edge boxes: Locating object proposals from edges. In: European conference on computer vision, pp. 391–405. Springer, Cham

  72. Zou Z, Shi Z, Guo Y, Ye J (2019) Object detection in 20 years: a survey. arXiv preprint arXiv:1905.05055

Download references

Acknowledgements

The authors thankful to all reviewers for their thorough reading of this manuscript and for the thoughtful comments and constructive suggestions, which help us to improve the quality of the manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Vigan’s Foundation for Science, Technology & Research (Deemed to be University), Vadlamudi, Guntur, AP, India

    Sivadi Balakrishna & Ahmad Abubakar Mustapha

Authors
  1. Sivadi Balakrishna
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ahmad Abubakar Mustapha
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

The authors confirm contribution to the paper as follows:

Sivadi Balakrishna- Study Conceptualization & design, methodology, investigation, writing—review and editing, supervision.

Ahmad Abubakar Mustapha- Data Collection, software, validation, formal analysis, resources, writing—original draft preparation.

Corresponding author

Correspondence to Sivadi Balakrishna.

Ethics declarations

Conflicts of interest

The authors of the manuscript declare that there is no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Balakrishna, S., Mustapha, A.A. Progress in multi-object detection models: a comprehensive survey. Multimed Tools Appl 82, 22405–22439 (2023). https://doi.org/10.1007/s11042-022-14131-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14131-0

Keywords