[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

LEN-YOLO: a lightweight remote sensing small aircraft object detection model for satellite on-orbit detection

  • Research
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

The performance of conventional detection algorithms in small aircraft target detection is often unsatisfactory due to the intricate backgrounds of remote sensing images and the diminutive size of aircraft targets. Furthermore, prevalent deep learning algorithms typically prove overly complex for integration into resource-constrained satellite platforms. In response to these challenges, an enhanced algorithm named LEN-YOLO (Lite backbone - Enhanced Neck - YOLO) has been devised to enhance detection accuracy while preserving model simplicity for the detection of small aircraft in satellite on-orbit scenarios. First, the EIoU Loss is adopted for target localization, enabling the network to effectively focus on small aircraft targets. Second, a Lite backbone is designed by discarding high semantic information, using low-semantic feature maps to detect small targets. Finally, a Bidirectional Weighted FPN based on SimAM and GSConv (BSG-FPN) is proposed to fuse feature maps of different scales to increase detailed information. Experimental results on RSOD and DIOR datasets demonstrate compared to the baseline YOLOv5, LEN-YOLO achieves an increase of 5.1% and 4.2% in \(\text {AP}_s\) respectively. Notably, parameters are reduced by 78.3% and floating-point operations by 33.2%.

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
£29.99 /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 includes VAT (United Kingdom)

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 availibility

No datasets were generated or analysed during the current study.

References

  1. Li, K., Wan, G., Cheng, G., Meng, L., Han, J.: Object detection in optical remote sensing images: a survey and a new benchmark. ISPRS J. Photogramm. Remote. Sens. 159, 296–307 (2020)

    Article  MATH  Google Scholar 

  2. Shi, L., Tang, Z., Wang, T., Xu, X., Liu, J., Zhang, J.: Aircraft detection in remote sensing images based on deconvolution and position attention. Int. J. Remote Sens. 42(11), 4241–4260 (2021)

    Article  MATH  Google Scholar 

  3. Wan, D., Lu, R., Wang, S., Shen, S., Xu, T., Lang, X.: Yolo-hr: improved yolov5 for object detection in high-resolution optical remote sensing images. Remote Sensing 15(3), 614 (2023)

    Article  Google Scholar 

  4. Zou, Z., Chen, K., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: A survey. Proceedings of the IEEE (2023)

  5. Girshick, R., Donahue, J., Darrell, T., Malik, J.: 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 (2014)

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

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

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

  9. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263–7271 (2017)

  10. Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018). Accessed 15 Nov 2022

  11. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020). Accessed 10 Nov 2022

  12. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37. Springer, Cham. (2016)

  13. Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 9627–9636 (2019)

  14. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)

  15. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European conference on computer vision, pp. 213–229. Springer (2020)

  16. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020). Accessed 24 Nov 2023

  17. Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J.: Detrs beat yolos on real-time object detection. arXiv preprint arXiv:2304.08069 (2023). Accessed 21 Nov 2023

  18. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117–2125 (2017)

  19. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8759–8768 (2018)

  20. Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10781–10790 (2020)

  21. Zhu, X., Lyu, S., Wang, X., Zhao, Q.: Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 2778–2788 (2021)

  22. Zhang, Y.F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient iou loss for accurate bounding box regression. Neurocomputing 506, 146–157 (2022)

    Article  MATH  Google Scholar 

  23. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017). Accessed 10 Nov 2022

  24. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510–4520 (2018)

  25. Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 1314–1324 (2019)

  26. Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

  27. Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)

  28. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: More features from cheap operations. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1580–1589 (2020)

  29. Liu, W., Tian, J., Tian, T.: Yolm: A remote sensing aircraft detection model. In: IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, pp. 1708–1711. IEEE (2022)

  30. Zhou, L., Yan, H., Zheng, C., Rao, X., Li, Y., Yang, W., Tian, J., Fan, M., Zuo, X.: Aircraft detection for remote sensing image based on bidirectional and dense feature fusion. Computational Intelligence and Neuroscience 2021 (2021)

  31. Zhang, Y., Song, C., Zhang, D.: Small-scale aircraft detection in remote sensing images based on faster-rcnn. Multimed Tools Appl 81(13), 18091–18103 (2022)

    Article  MATH  Google Scholar 

  32. Ma, Y., Zhou, D., He, Y., Zhao, L., Cheng, P., Li, H., Chen, K.: Aircraft-lbdet: multi-task aircraft detection with landmark and bounding box detection. Remote Sens 15(10), 2485 (2023)

    Article  MATH  Google Scholar 

  33. Yu, L., Zhi, X., Hu, J., Zhang, S., Niu, R., Zhang, W., Jiang, S.: Improved deformable convolution method for aircraft object detection in flight based on feature separation in remote sensing images. IEEE J Sel Topics Appl Earth Observ Remote Sens 17, 8313–8323 (2024). https://doi.org/10.1109/JSTARS.2024.3386696

    Article  MATH  Google Scholar 

  34. Hoanh, N., Pham, T.V.: A multi-task framework for car detection from high-resolution uav imagery focusing on road regions. IEEE Transactions on Intelligent Transportation Systems pp. 1–14 (2024). https://doi.org/10.1109/TITS.2024.3432761

  35. Liu, Q., Liu, R., Zheng, B., Wang, H., Fu, Y.: Infrared small target detection with scale and location sensitivity (2024). arXiv:2403.19366. Accessed 5 Apr 2024

  36. Chirgaiya, S., Rajavat, A.: Tiny object detection model based on competitive multi-layer neural network (tod-cmlnn). Intelligent Systems with Applications 18, 200217 (2023), https://doi.org/10.1016/j.iswa.2023.200217. https://www.sciencedirect.com/science/article/pii/S266730532300042X. Accessed 23 Sept 2024

  37. Liu, D., Zhang, J., Qi, Y., Wu, Y., Zhang, Y.: Tiny object detection in remote sensing images based on object reconstruction and multiple receptive field adaptive feature enhancement. IEEE Trans. Geosci. Remote Sens. 62, 1–13 (2024). https://doi.org/10.1109/TGRS.2024.3381774

    Article  MATH  Google Scholar 

  38. Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., Ren, Q.: Slim-neck by gsconv: A better design paradigm of detector architectures for autonomous vehicles. arXiv preprint arXiv:2206.02424 (2022). Accessed 7 Oct 2023

  39. Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015). Accessed 10 June 2024

  40. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015). Accessed 10 June 2024

  41. Yang, G., Lei, J., Zhu, Z., Cheng, S., Feng, Z., Liang, R.: Afpn: asymptotic feature pyramid network for object detection. In: 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2184–2189. IEEE (2023)

  42. Xiao, J., Guo, H., Zhou, J., Zhao, T., Yu, Q., Chen, Y., Wang, Z.: Tiny object detection with context enhancement and feature purification. Expert Syst. Appl. 211, 118665 (2023)

    Article  Google Scholar 

  43. Shi, Q., Li, L., Feng, J., Chen, W., Yu, J.: Automated model hardening with reinforcement learning for on-orbit object detectors with convolutional neural networks. Aerospace 10(1) (2023). https://doi.org/10.3390/aerospace10010088. https://www.mdpi.com/2226-4310/10/1/88. Accessed 29 Sept 2024

  44. Xu, P., Li, Q., Zhang, B., Wu, F., Zhao, K., Du, X., Yang, C., Zhong, R.: On-board real-time ship detection in hisea-1 sar images based on cfar and lightweight deep learning. Remote Sensing 13(10) (2021). https://doi.org/10.3390/rs13101995. https://www.mdpi.com/2072-4292/13/10/1995. Accessed 29 Sept 2024

  45. Zhou, Q., Cui, H., Liang, S., Li, H.: An on-orbit target detection method in remote sensing images for micro satellite. J. Phys: Conf. Ser. 2006(1), 012030 (2021). https://doi.org/10.1088/1742-6596/2006/1/012030

    Article  MATH  Google Scholar 

  46. Pang, Y., Zhang, Y., Kong, Q., Wang, Y., Chen, B., Cao, X.: Socdet: a lightweight and accurate oriented object detection network for satellite on-orbit computing. IEEE Trans. Geosci. Remote Sens. 61, 1–15 (2023). https://doi.org/10.1109/TGRS.2023.3269642

    Article  MATH  Google Scholar 

  47. Jocher, G.: Ultralytics yolov5 (2020). https://doi.org/10.5281/zenodo.3908559. https://github.com/ultralytics/yolov5. Accessed 10 Nov 2022

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

    Article  MATH  Google Scholar 

  49. Yang, L., Zhang, R.Y., Li, L., Xie, X.: Simam: A simple, parameter-free attention module for convolutional neural networks. In: International conference on machine learning, pp. 11863–11874. PMLR (2021)

  50. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 658–666 (2019)

  51. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-iou loss: Faster and better learning for bounding box regression. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp. 12993–13000 (2020)

  52. Zheng, Z., Wang, P., Ren, D., Liu, W., Ye, R., Hu, Q., Zuo, W.: Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE transactions on cybernetics 52(8), 8574–8586 (2021)

    Article  MATH  Google Scholar 

  53. Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: 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 (2020)

  54. Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

  55. Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLO (2023). https://github.com/ultralytics/ultralytics

  56. Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 9627–9636 (2019)

  57. Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 6569–6578 (2019)

  58. Yang, Z., Liu, S., Hu, H., Wang, L., Lin, S.: Reppoints: Point set representation for object detection. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 9657–9666 (2019)

  59. Cheng, G., Han, J., Zhou, P., Guo, L.: Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS J. Photogramm. Remote. Sens. 98, 119–132 (2014)

    Article  MATH  Google Scholar 

  60. Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., Jiao, J.: Orientation robust object detection in aerial images using deep convolutional neural network. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 3735–3739. IEEE (2015)

Download references

Acknowledgements

We are grateful to the micro-satellite Research Center of Zhejiang University for the assistance with computations.

Author information

Authors and Affiliations

  1. Micro-satellite Research Center, Zhejiang University, Hangzhou, 310027, China

    Jian Wu, Fanyu Zhao & Zhonghe Jin

Authors
  1. Jian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fanyu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhonghe Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Wu was responsible for the conception and design of the study, data collection, and drafting the initial manuscript. Zhao was involved in data analysis and interpretation, provided critical academic insights, and contributed to the revision of the manuscript. Jin ensured the smooth progress of the project, and also reviewed and approved the final manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Fanyu Zhao.

Ethics declarations

Conflict of interest

The authors declare that they have 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

Wu, J., Zhao, F. & Jin, Z. LEN-YOLO: a lightweight remote sensing small aircraft object detection model for satellite on-orbit detection. J Real-Time Image Proc 22, 25 (2025). https://doi.org/10.1007/s11554-024-01601-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11554-024-01601-x

Keywords

Search

Navigation