Skip to main content
SpringerLink
Log in

ASSD-YOLO: a small object detection method based on improved YOLOv7 for airport surface surveillance

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

Abstract

The airport surface surveillance system is essential in ensuring airport safety and maximizing the efficient utilization of airport resources. Current airport detection algorithms suffer from few and lack relevant airport target data. To solve these issues, this paper establishes two airport datasets named ASS-Dataset, including the surveillance dataset and the panoramic surveillance dataset. Compared to other aircraft datasets, our datasets are collected from authentic airport surface surveillance systems. According to observation, most objects are small in datasets. This paper proposes a small object detection method ASSD-YOLO based on improved YOLOv7. First, the designed f-efficient attention module is added to the backbone network to improve the accuracy of the algorithm. Second, the transformer encoder network is incorporated into the backbone network to increase feature extraction. Finally, the small target detection layer is added to the head network to improve the ability to extract small targets. The model of the mean average precision is 93.5\(\%\) in the surveillance dataset. In the panoramic surveillance dataset, the ASSD-YOLO achieves 10.8\(\%\) and 21.4\(\%\) higher average precision for the airplane and truck than YOLOv7. g. Comparing the method proposed in this paper to the original YOLOv7, the performance improvement for the mAP is 4.6\(\%\) when using the RSOD open dataset. ASS-Dataset is available at https://github.com/rookie257/rookie257.github.io. Code is available at https://github.com/rookie257/small_detection.github.io.

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

Access this article

Log in via an institution

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
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data Availability

Data is available at https://github.com/rookie257/rookie257.github.io.

Code Availability

Code is available at https://github.com/rookie257/small_detection.github.io.

References

  1. Wilke S, Majumdar A, Ochieng WY (2015) The impact of airport characteristics on airport surface accidents and incidents. J Safety Res 53:63–75

    Article  Google Scholar 

  2. Wang Y, Li MZ, Gopalakrishnan K, Liu T (2022) Timescales of delay propagation in airport networks. Transportation Research Part E: Logistics and Transportation Review 161:102687

    Article  Google Scholar 

  3. Morris R, Pasareanu CS, Luckow KS, Malik W, Ma H, Kumar TS, Koenig S (2016) Planning, scheduling and monitoring for airport surface operations. In: AAAI workshop: planning for hybrid systems, pp 608–614

  4. 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

  5. Redmon J, Farchadi A (2017) Yolo9000: better, faster, stronger 2017 IEEE conference on computer vision and pattern recognition (CVPR). Go to reference in article

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

  7. Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934

  8. Li C, Li L, Jiang H, Weng K, Geng Y, Li L, Ke Z, Li Q, Cheng M, Nie W et al (2022) Yolov6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976

  9. Wang C-Y, Bochkovskiy A, Liao H-YM (2023) Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7464–7475

  10. Bie M, Liu Y, Li G, Hong J, Li J (2023) Real-time vehicle detection algorithm based on a lightweight you-only-look-once (yolov5n-l) approach. Expert Syst Appl 213:119108

    Article  Google Scholar 

  11. Shao Y, Zhang X, Chu H, Zhang X, Zhang D, Rao Y (2022) Air-yolov3: aerial infrared pedestrian detection via an improved yolov3 with network pruning. Appl Sci 12(7):3627

    Article  Google Scholar 

  12. Tang Y, Zhou H, Wang H, Zhang Y (2023) Fruit detection and positioning technology for a camellia oleifera c. abel orchard based on improved yolov4-tiny model and binocular stereo vision. Expert systems with applications 211:118573

    Article  Google Scholar 

  13. Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: common objects in context. In: Computer vision–ECCV 2014: 13th European conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. Springer, pp 740–755

  14. Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vision 88:303–338

    Article  Google Scholar 

  15. Liu M, Wang X, Zhou A, Fu X, Ma Y, Piao C (2020) Uav-yolo: small object detection on unmanned aerial vehicle perspective. Sensors 20(8):2238

    Article  Google Scholar 

  16. Benjumea A, Teeti I, Cuzzolin F, Bradley A (2021) Yolo-z: improving small object detection in yolov5 for autonomous vehicles. arXiv preprint arXiv:2112.11798

  17. Wang X, Zhao Q, Jiang P, Zheng Y, Yuan L, Yuan P (2022) Lds-yolo: a lightweight small object detection method for dead trees from shelter forest. Comput Electron Agric 198:107035

    Article  Google Scholar 

  18. 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

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

  20. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Advances in neural information processing systems 28

  21. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969

  22. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: single shot multibox detector. In: Computer vision–ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer, pp 21–37

  23. Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988

  24. Law H, Deng J (2018) Cornernet: detecting objects as paired keypoints. In: Proceedings of the European conference on computer vision (ECCV), pp 734–750

  25. Zhang J, Huo Y-B, Yang J-L, Wang X-Z, Yan B-Y, Du X-H, Hao R-Q, Yang F, Liu J-X, Liu L et al (2022) Automatic counting of retinal ganglion cells in the entire mouse retina based on improved yolov5. Zool Res 43(5):738

    Article  Google Scholar 

  26. Kim M, Jeong J, Kim S (2021) Ecap-yolo: efficient channel attention pyramid yolo for small object detection in aerial image. Remote Sensing 13(23):4851

    Article  Google Scholar 

  27. Junos MH, Mohd Khairuddin AS, Thannirmalai S, Dahari M (2021) Automatic detection of oil palm fruits from uav images using an improved yolo model. The visual computer, 1–15

  28. Lim J-S, Astrid M, Yoon H-J, Lee S-I (2021) Small object detection using context and attention. In: 2021 International conference on artificial intelligence in information and communication (ICAIIC). IEEE, pp 181–186

  29. Cheng G, Yuan X, Yao X, Yan K, Zeng Q, Xie X, Han J (2023) Towards large-scale small object detection: survey and benchmarks. IEEE Transactions on Pattern Analysis and Machine Intelligence

  30. Mahaur B, Mishra K (2023) Small-object detection based on yolov5 in autonomous driving systems. Pattern Recogn Lett 168:115–122

    Article  Google Scholar 

  31. Yuan X, Cheng G, Yan K, Zeng Q, Han J (2023) Small object detection via coarse-to-fine proposal generation and imitation learning. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 6317–6327

  32. Chen C, Liu M-Y, Tuzel O, Xiao J (2017) R-cnn for small object detection. In: Computer vision–ACCV 2016: 13th Asian conference on computer vision, Taipei, Taiwan, November 20-24, 2016, Revised selected papers, Part V 13. Springer, pp 214–230

  33. Kumar A (2023) Seat-yolo: a squeeze-excite and spatial attentive you only look once architecture for shadow detection. Optik, 170513

  34. Kumar A, Kalia A, Verma K, Sharma A, Kaushal M (2021) Scaling up face masks detection with yolo on a novel dataset. Optik 239:166744

    Article  Google Scholar 

  35. Li C, Cai C (2023) A calibration and real-time object matching method for heterogeneous multi-camera system. IEEE Trans Instrum Meas 72:1–12

    Google Scholar 

  36. Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19

  37. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 11534–11542

  38. Qiu S, Xu X, Cai B (2018) Frelu: flexible rectified linear units for improving convolutional neural networks. In: 2018 24th International conference on pattern recognition (icpr). IEEE, pp 1223–1228

  39. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  40. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30

  41. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929

  42. Liu Q, Zhang Y, Yang G (2023) Small unopened cotton boll counting by detection with mrf-yolo in the wild. Comput Electron Agric 204:107576

    Article  Google Scholar 

  43. Chen Z, Liu C, Filaretov V, Yukhimets D (2023) Multi-scale ship detection algorithm based on yolov7 for complex scene sar images. Remote Sensing 15(8):2071

    Article  Google Scholar 

  44. Long Y, Gong Y, Xiao Z, Liu Q (2017) Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 55(5):2486–2498

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Key R &D Program of China(No.2021YFF0603904) and the Key Projects of Heilongjiang Provincial Natural Science Foundation(No.ZD2022F001).

Author information

Author notes

  1. Chengtao Cai and Liying Zheng contributed equally to this work.

Authors and Affiliations

  1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Wentao Zhou, Chengtao Cai, Chenming Li & Daohui Zeng

  2. Heilongjiang Provincial Key Laboratory of Environment Intelligent Perception, Harbin Engineering University, Harbin, 150001, China

    Chengtao Cai

  3. Key laboratory of Intelligent Technology and Application of Marine Equipment (Harbin Engineering University), Harbin Engineering University, Harbin, 150001, China

    Chengtao Cai

  4. College of Computer Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Liying Zheng

Authors
  1. Wentao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengtao Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liying Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daohui Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, Wentao Zhou, Chengtao Cai and Liying Zheng; data curation, Liying Zheng; formal analysis, Wentao Zhou and Chengtao Cai; investigation, Wentao Zhou and Liying Zheng; methodology, Wentao Zhou and Chengtao Cai; project administration, Liying Zheng and Chenming Li; writings original draft, Wentao Zhou and Daohui Zeng; writing, reviewing and editing, Chenming Li. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Chengtao Cai or Liying Zheng.

Ethics declarations

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Appendix A: Section title of first appendix

Appendix A: Section title of first appendix

An appendix contains supplementary information that is not an essential part of the text itself but which may be helpful in providing a more comprehensive understanding of the research problem or it is information that is too cumbersome to be included in the body of the paper.

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

Zhou, W., Cai, C., Zheng, L. et al. ASSD-YOLO: a small object detection method based on improved YOLOv7 for airport surface surveillance. Multimed Tools Appl 83, 55527–55548 (2024). https://doi.org/10.1007/s11042-023-17628-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17628-4

Keywords

Search

Navigation