Skip to main content

Advertisement

Springer Nature Link
Log in

ADH-YOLO: a small object detection based on improved YOLOv8 for airport scene images in hazy weather

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Accurately obtaining airport objects is crucial to ensuring airport safety and improving airport efficiency. The previous research has found that airport scene images have so many small objects. The presence of hazy reduces visibility and affects the ability to obtain information about objects in airport scene images. To address the challenges, this article proposes a small object detection based on improved YOLOv8 for airport scene images in hazy weather (ADH-YOLO). Firstly, this article constructs HASS1, HASS2, and HRSOD hazy datasets based on the HAZERD method. We find that YOLOv8 which is anchor-free detection shows significant performance improvement after using anchor boxes in hazy datasets. Then, to adapt to hazy environments, we design a decoupled detection head with the attention module (DDAH) and add a coordinate attention (CA) module to the backbone network. A small object detection layer is added to the original structure to further improve the performance of small object detection. Finally, this article uses partial convolution (PConv) to balance detection performance and computational resource consumption to reconstruct the backbone and neck C2F modules. We propose two lite versions of ADH-YOLO (LADH-YOLOn and LADH-YOLOa) compared with the original YOLOv8x, in which Params decreased by 22.1% and 49.6%, with higher mAP values. The proposed method improves mAP by 10.5% and 31.2% on the HASS1 and HASS2. Generalization experiments show that the proposed method achieves 96.1% mAP optimal detection results compared to classical detection methods on the HRSOD. The source code is available at https://github.com/rookie257/HAD-YOLO.

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

Similar content being viewed by others

Data availability

The code is available at https://github.com/rookie257/HAD-YOLO. The ASS dataset is available at https://github.com/rookie257/rookie257.github.io.

References

  1. Durso FT, Manning CA (2008) Air traffic control. Rev Hum Factors Ergon 4(1):195–244

    Article  Google Scholar 

  2. Li H, Li J, Zhao X, Kuang X (2022) The morphological structure and influence factors analysis of China’s domestic civil aviation freight transport network. Transp Policy 125:207–217

    Article  MATH  Google Scholar 

  3. Zhou W, Cai C, Wu K, Li C, Gao B (2024) Las-yolo: a lightweight detection method based on yolov7 for small objects in airport surveillance. J Supercomput 1–26

  4. Chen Z, Wang X, Fan T, Xu L (2020) Color-depth multi-task learning for object detection in haze. Neural Comput Appl 32:6591–6599

    Article  MATH  Google Scholar 

  5. Li C, Yu H, Zhou S, Liu Z, Guo Y, Yin X, Zhang W (2023) Efficient dehazing method for outdoor and remote sensing images. IEEE J Sel Top Appl Earth Observ Remote Sens 16:4516–4528

    Article  MATH  Google Scholar 

  6. Zheng Y, Su J, Zhang S, Tao M, Wang L (2024) Dehaze-tggan: transformer-guide generative adversarial networks with spatial-spectrum attention for unpaired remote sensing dehazing. IEEE Trans Geosci Remote Sens

  7. Zheng Y, Su J, Zhang S, Tao M, Wang L (2024) Unpaired remote sensing image dehazing using enhanced skip attention-based generative adversarial networks with rotation invariance. Remote Sens 16(15):2707

    Article  MATH  Google Scholar 

  8. Zhang Q, Hu, X (2023) Msffa-yolo network: multi-class object detection for traffic investigations in foggy weather. IEEE Trans Instrum Meas

  9. Shamsollahi D, Moselhi O, Khorasani K (2024) Data integration using deep learning and real-time locating system (rtls) for automated construction progress monitoring and reporting. Autom Constr 168:105778

    Article  MATH  Google Scholar 

  10. Yu L, Zhi X, Hu J, Zhang S, Niu R, Zhang W, Jiang S (2024) An improved deformable convolution method for aircraft object detection in flight based on feature separation in remote sensing images. IEEE J Sel Top Appl Earth Observ Remote Sens

  11. 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

  12. Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1440–1448

  13. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28

  14. 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

  15. Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q (2019) Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 6569–6578

  16. Zhang Y, Ye M, Zhu G, Liu Y, Guo P, Yan J (2024) Ffca-yolo for small object detection in remote sensing images. IEEE Trans Geosci Remote Sens

  17. Wu T, Zhou Z, Liu J, Zhang D, Fu Q, Ou Y, Jiao R (2024) Ise-yolo: a real-time infrared detection model for substation equipment. IEEE Trans Power Deliv

  18. Han Y, Wang F, Wang W, Li X, Zhang J (2024) Yolo-sg: small traffic signs detection method in complex scene. J Supercomput 80(2):2025–2046

    Article  MATH  Google Scholar 

  19. Liu B, Chen S-B, Wang J-X, Tang J, Luo B (2024) An oriented object detector for hazy remote sensing images. IEEE Trans Geosci Remote Sens

  20. Redmon J (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

  21. Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7263–7271

  22. Redmon J (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767

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

  24. 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

  25. Terven J, Córdova-Esparza D-M, Romero-González J-A (2023) A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas. Mach Learn Knowl Extract 5(4):1680–1716

    Article  Google Scholar 

  26. Tong K, Wu Y (2024) I-yolo: a novel single-stage framework for small object detection. Vis Comput 1–18

  27. Xue C, Xia Y, Wu M, Chen Z, Cheng F, Yun L (2024) El-yolo: an efficient and lightweight low-altitude aerial objects detector for onboard applications. Expert Syst Appl 256:124848

    Article  Google Scholar 

  28. Zhang Y, Ding L, Sharma G (2017) Hazerd: an outdoor scene dataset and benchmark for single image dehazing. In: 2017 IEEE International Conference on Image Processing (ICIP), IEEE, pp 3205–3209

  29. Koschmieder H (1924) Theorie der horizontalen sichtweite, beitrage zur physik der freien atmosphare. Meteorol Z 12:3353

    MATH  Google Scholar 

  30. Zhao A, Li L, Liu S (2024) Uidf-net: unsupervised image dehazing and fusion utilizing gan and encoder-decoder. J Imaging 10(7):164

    Article  MATH  Google Scholar 

  31. Song R, Li T, Li T (2023) Ship detection in haze and low-light remote sensing images via colour balance and dcnn. Appl Ocean Res 139:103702

    Article  MATH  Google Scholar 

  32. Zhang Y, Liu H, Dong D, Duan X, Lin F, Liu Z (2024) Dpf-yolov8: dual path feature fusion network for traffic sign detection in hazy weather. Electronics 13(20):4016

    Article  Google Scholar 

  33. Deng H, Zhang Y (2023) Fmr-yolo: infrared ship rotating target detection based on synthetic fog and multi-scale weighted feature fusion. IEEE Trans Instrum Meas

  34. 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

  35. Zhang S, Chi C, Yao Y, Lei Z, Li SZ (2020) Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 9759–9768

  36. Wang C, Yeh I, Liao H (2024) Yolov9: learning what you want to learn using programmable gradient information. arXiv preprint arXiv:2402.13616

  37. Kong T, Sun F, Liu H, Jiang Y, Li L, Shi J (2020) Foveabox: Beyound anchor-based object detection. IEEE Trans Image Process 29:7389–7398

    Article  Google Scholar 

  38. Xiong X, He M, Li T, Zheng G, Xu W, Fan X, Zhang Y (2024) Adaptive feature fusion and improved attention mechanism based small object detection for uav target tracking. IEEE Internet Things J

  39. Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 13713–13722

  40. Chen J, Kao S-h, He H, Zhuo W, Wen S, Lee C-H, Chan S-HG (2023) Run, don’t walk: chasing higher flops for faster neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12021–12031

  41. Zhou W, Cai C, Zheng L, Li C, Zeng D (2024) Assd-yolo: a small object detection method based on improved yolov7 for airport surface surveillance. Multimedia Tools Appl 83(18):55527–55548

    Article  Google Scholar 

  42. Sun W, Dai L, Zhang X, Chang P, He X (2022) Rsod: real-time small object detection algorithm in uav-based traffic monitoring. Appl Intell 1–16

  43. Liu H, Xiong W, Zhang Y (2023) Yolo-core: contour regression for efficient instance segmentation. Mach Intell Res 20(5):716–728

    Article  MATH  Google Scholar 

  44. Zhu W, Yang Z (2024) Csb-yolo: a rapid and efficient real-time algorithm for classroom student behavior detection. J Real-Time Image Proc 21(4):140

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was 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

Authors and Affiliations

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

    Wentao Zhou, Chengtao Cai, Zijian Cui & Chenming Li

  2. Department of Mechanical Engineering, National University of Singapore, Singapore, 119260, Singapore

    Wentao Zhou & Sutthiphong Srigrarom

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

    Chengtao Cai

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

    Chengtao Cai

  5. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Pengfei Wang

Authors
  1. Wentao Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chengtao Cai
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Sutthiphong Srigrarom
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Pengfei Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Zijian Cui
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Chenming Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Wentao Zhou helped in conceptualization, data curation, software, investigation, methodology, formal analysis, project administration, writing an original draft, and writing a review and editing the initial draft. Chengtao Cai and Sutthiphong Srigrarom helped in conceptualization, methodology, software, investigation, validation, formal analysis, and project administration. Pengfei Wang contributed to writing a review and editing the initial draft. Chenming Li helped in conceptualization, methodology, software, investigation, validation, and writing an original draft and a review. Zijian Cui worked in supervision and writing an initial draft.

Corresponding authors

Correspondence to Chengtao Cai or Sutthiphong Srigrarom.

Ethics declarations

Conflict of 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 article.

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

Zhou, W., Cai, C., Srigrarom, S. et al. ADH-YOLO: a small object detection based on improved YOLOv8 for airport scene images in hazy weather. J Supercomput 81, 505 (2025). https://doi.org/10.1007/s11227-025-06999-0

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-025-06999-0

Keywords