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Vehicle Detection in Aerial Images: A Survey

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Data Science and Communication (ICTDsC 2023)

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Abstract

In a variety of computer vision-based applications, one of the most crucial jobs is the recognition of vehicles from unmanned aerial vehicle (UAV) imagery. High accuracy and speed were required to do this critical assignment. Aerial image properties and the technology being utilized, such as diverse automobile sizes and their orientations, densities, small datasets, and inference speed, make this a highly difficult assignment. Numerous approaches based on hand-crafted feature design and deep learning have been put out in the literature as solutions to these issues in recent years. Techniques based on hand-crafted features and shallow learning have limited applicability to other complicated scenarios. On the other hand, Due to deep learning's robust learning capability, vehicle detection algorithms produced superior outcomes. In this article, we reviewed the hand-crafted feature design-based and data-driven vehicle detection algorithms from UAV imagery. We start by presenting various hand-crafted design-based vehicle detection algorithms like Histogram of oriented gradients (HOG), Scale Invariant Feature Transform (SIFT), and Local Binary Pattern (LBP). After that, we reviewed one-stage and two-stage vehicle detection series based on deep learning. One-stage includes state-of-art algorithms like YOLO and SSD, and the two-stage series includes object detection models like R-CNN, Fast R-CNN, Faster-RCNN, etc. We also focused on investigating the various public aerial image datasets and their related work. In the end, we have also shed some insight into the conclusion of this paper.

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References

  1. Benjdira B, Khursheed T, Koubaa A, Ammar A, Ouni K (2019) Car detection using unmanned aerial vehicles: Comparison between faster r-cnn and yolov3.In: 1st international conference on unmanned vehicle systems-Oman (UVS), pp 1–6 IEEE

    Google Scholar 

  2. Duarte D, Nex F, Kerle N, Vosselman G (2018) Satellite image classification of building damages using airborne and satellite image samples in a deep learning approach. ISPRS Ann Photogram Remote Sens Spat Inf Sci 4(2)

    Google Scholar 

  3. Ji H, GAO Z, Mei T, Li Y (2019) Improved faster R-CNN with multiscale feature fusion and homography augmentation for vehicle detection in remote sensing images. IEEE Geosci Remote Sens Lett 16(11):1761–1765

    Google Scholar 

  4. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE computer society conference on computer vision and pattern recognition (CVPR'05), Vol 1, pp 886–893

    Google Scholar 

  5. Pietikäinen M (2005) Image analysis with local binary patterns. In: image analysis: 14th scandinavian conference, SCIA 2005, Joensuu, Finland, June 19–22, 2005. Proceedings 14, pp 115–118

    Google Scholar 

  6. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110

    Article  Google Scholar 

  7. Long J, Shelhamer E, Darrell (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

    Google Scholar 

  8. Chen X, Xiang S, Liu CL, Pan CH (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci Remote Sens Lett 11(10):1797–1801

    Article  Google Scholar 

  9. Cheng G, Han J, Lu X (2017) Remote sensing image scene classification: benchmark and state of the art. Proc IEEE 105(10):1865–1883

    Article  Google Scholar 

  10. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

    Google Scholar 

  11. 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 

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

    Google Scholar 

  13. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Adv Neural Information Process sys

    Google Scholar 

  14. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In European conference on computer vision, pp 21–37

    Google Scholar 

  15. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788

    Google Scholar 

  16. https://neptune.ai/blog/object-detection-algorithms-and-libraries/

  17. Liu K, Mattyus G (2015) Fast multiclass vehicle detection on aerial images. IEEE Geosci Remote Sens Lett 12(9):1938–1942

    Article  Google Scholar 

  18. SIFT | How to use SIFT for image matching in python (analyticsvidhya.com) (2019)

    Google Scholar 

  19. Moranduzzo T, Melgani F (2013) Automatic car counting method for unmanned aerial vehicle images. IEEE Trans Geosci Remote Sens 52(3):1635–1647

    Article  Google Scholar 

  20. Hassaballah M, Kenk MA, El-Henawy IM (2020) Local binary pattern-based on-road vehicle detection in urban traffic scene. Pattern Anal Appl 23(4):1505–1521

    Article  Google Scholar 

  21. Cheng HY, Weng CC, Chen YY (2011) Vehicle detection in aerial surveillance using dynamic Bayesian networks. IEEE Trans Image Process 21(4):2152–2159

    Article  MathSciNet  Google Scholar 

  22. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  23. Deng Z, Sun H, Zhou S, Zhao J, Zou H (2017) Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks. IEEE J Sel Top Appl Earth Obs Remote Sen 10(8):3652–3664

    Article  Google Scholar 

  24. Wang L, Liao J, Xu C (2019) Vehicle detection based on drone images with the improved faster R-CNN. In: Proceedings of the 11th international conference on machine learning and computing, pp 466–471

    Google Scholar 

  25. https://blog.paperspace.com/faster-r-cnn-explained-object-detection/

  26. Redmon J, Farhadi A (2016) OLO9000: better, faster, stronger; CoRR. 1612.08242

    Google Scholar 

  27. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement.1804.02767

    Google Scholar 

  28. Xu B, Wang B, Gu Y (2019) Vehicle detection in aerial images using modified YOLO. In: 19th international conference on communication technology (ICCT), pp 1669–1672

    Google Scholar 

  29. Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection.2004.10934

    Google Scholar 

  30. Jocher G, Nishimura K, Mineeva T, Vilarino R (2020) Yolov5 by ultralytics. Disponıvel em: https://github.com/ultralytics/yolov5

  31. Ammar A, Koubaa A, Ahmed M, Saad A, Benjdira B (2021) Vehicle detection from aerial images using deep learning. A comparative study. Electronics 10(7):820

    Google Scholar 

  32. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer vision, pp 21–37

    Google Scholar 

  33. Mansour A, Hassan A, Hussein WM, Said E (2019) Automated vehicle detection in satellite images using deep learning. Int Conf Aerosp Sci Aviat Technol 18(18):1–8

    Google Scholar 

  34. Sommer LW, Schuchert T, Beyerer J (2017) Deep learning based multi-category object detection in aerial images. Autom Target Recogn XXVII 10202:48–55

    Google Scholar 

  35. Razakarivony S, Jurie F (2016) Vehicle detection in aerial imagery: A small target detection benchmark. J Vis Commun Image Represent 34:187–203

    Article  Google Scholar 

  36. Kleber EJ, McKean AP, Hiscock AI, Hylland MD, Hardwick CL, McDonald GN, Erickson BA (2021) Geologic Setting, Ground Effects, and Proposed Structural Model for the 18 March 2020 Mw 5.7 Magna, Utah, Earthquake. Seismol Res Lett 92(2A):710–724

    Google Scholar 

  37. Tang T, Zhou S, Deng Z, Le L, Zou H (2017) Arbitrary-oriented vehicle detection in aerial imagery with single convolutional neural networks. Remote Sens 9(11):1170

    Article  Google Scholar 

  38. Xia GS, Bai X, Ding J, Zhu Z, Belongie S, Luo J, Zhang L (2018) DOTA: a large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3974–3983

    Google Scholar 

  39. Mundhenk TN, Konjevod G, Sakla WA, Boakye K (2016) A large contextual dataset for classification, detection and counting of cars with deep learning. In: European conference on computer vision, pp 785–800

    Google Scholar 

  40. Lu J, Ma C, Li L, Xing X, Zhang Y, Wang Z, Xu J (2018) A vehicle detection method for aerial image based on YOLO. J Comput Commun 6(11):98–107

    Article  Google Scholar 

  41. Zhu P, Wen L, Bian X, Ling H, Hu Q (2018) Vision meets drones: a challenge. arXiv preprint arXiv:1804.07437

  42. Bisio I, Haleem H, Garibotto C, Lavagetto F, Sciarrone A (2021) Performance evaluation and analysis of drone-based vehicle detection techniques from deep learning perspective. IEEE Internet Things J

    Google Scholar 

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Authors and Affiliations

  1. Department of the Computer Engineering, National Institute of Technology, Kurukshetra, Haryana, India

    Digvijay Kumar & Bharti Sinha

Authors
  1. Digvijay Kumar
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  2. Bharti Sinha
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Correspondence to Digvijay Kumar .

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Editors and Affiliations

  1. Faculty of Engineering, University of Porto (FEUP), Porto, Portugal

    João Manuel R. S. Tavares

  2. National Institute of Telecommunications (INATEL), Santa Rita do Sapucaí, Minas Gerais, Brazil

    Joel J. P. C. Rodrigues

  3. Siliguri Institute of Technology, Siliguri, West Bengal, India

    Debajyoti Misra

  4. Siliguri Institute of Technology, Siliguri, West Bengal, India

    Debasmriti Bhattacherjee

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Kumar, D., Sinha, B. (2024). Vehicle Detection in Aerial Images: A Survey. In: Tavares, J.M.R.S., Rodrigues, J.J.P.C., Misra, D., Bhattacherjee, D. (eds) Data Science and Communication. ICTDsC 2023. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-99-5435-3_10

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