YOLO-SSP: an object detection model based on pyramid spatial attention and improved downsampling strategy for remote sensing images (original) (raw)

References

  1. Qin, P., Cai, Y., Liu, J., Fan, P., Sun, M.: Multilayer feature extraction network for military ship detection from high-resolution optical remote sensing images. IEEE J. Sel. Top. Appl. Earth. Obs. Remote Sens. 14, 11058–11069 (2021)
    Article MATH Google Scholar
  2. Reedha, R., Dericquebourg, E., Canals, R., Hafiane, A.: Transformer neural network for weed and crop classification of high resolution UAV images. Remote Sens. 14(3), 592 (2022)
    Article Google Scholar
  3. Gagliardi, V., Tosti, F., Bianchini Ciampoli, L., Battagliere, M.L., D’Amato, L., Alani, A.M., Benedetto, A.: Satellite remote sensing and non-destructive testing methods for transport infrastructure monitoring: advances, challenges and perspectives. Remote Sens. 15(2), 418 (2023)
    Article Google Scholar
  4. Chen, F., Chen, X., Voorde, T., Roberts, D., Jiang, H., Xu, W.: Open water detection in urban environments using high spatial resolution remote sensing imagery. Remote Sens. Environ. 242, 111706 (2020)
    Article Google Scholar
  5. Singh, S.A., Desai, K.: Automated surface defect detection framework using machine vision and convolutional neural networks. J. Intell. Manuf. 34(4), 1995–2011 (2023)
    Article MATH Google Scholar
  6. Leng, J., Liu, Y., Du, D., Zhang, T., Quan, P.: Robust obstacle detection and recognition for driver assistance systems. IEEE Trans. Intell. Transp. Syst. 21(4), 1560–1571 (2019)
    Article MATH Google Scholar
  7. Li, J., Chen, J., Sheng, B., Li, P., Yang, P., Feng, D.D., Qi, J.: Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Trans. Ind. Inf. 18(1), 163–173 (2022)
    Article MATH Google Scholar
  8. Han, Z., Jian, M., Wang, G.-G.: Convunext: an efficient convolution neural network for medical image segmentation. Knowl. Based Syst. 253, 109512 (2022)
    Article MATH Google Scholar
  9. Pan, J., Sun, D., Zhang, J., Tang, J., Yang, J., Tai, Y.-W., Yang, M.-H.: Dual convolutional neural networks for low-level vision. Int. J. Comput. Vis. 130(6), 1440–1458 (2022)
    Article MATH Google Scholar
  10. Leng, J., Liu, Y., Gao, X., Wang, Z.: Crnet: context-guided reasoning network for detecting hard objects. IEEE Trans. Multimed. 26, 3765–3777 (2024)
    Article MATH Google Scholar
  11. Zou, Z., Chen, K., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: a survey. Proc. IEEE 111(3), 257–276 (2023)
    Article MATH Google Scholar
  12. 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)
  13. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2015)
    Article Google Scholar
  14. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
    Article MATH Google Scholar
  15. Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
  16. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, 91–99 (2015)
  17. 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)
  18. 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)
  19. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: single shot multibox detector. In: Proceedings of the European Conference on Computer Vision, pp. 21–37 (2016)
  20. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
  21. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proceedings of the European Conference on Computer Vision, pp. 213–229 (2020)
  22. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017)
  23. Jian, L., Pu, Z., Zhu, L., Yao, T., Liang, X.: Ss R-CNN: self-supervised learning improving mask R-CNN for ship detection in remote sensing images. Remote Sens. 14(17), 4383 (2022)
    Article MATH Google Scholar
  24. Cheng, G., Yan, B., Shi, P., Li, K., Yao, X., Guo, L., Han, J.: Prototype-CNN for few-shot object detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1–10 (2021)
    MATH Google Scholar
  25. Leng, J., Mo, M., Zhou, Y., Gao, C., Li, W., Gao, X.: Pareto refocusing for drone-view object detection. IEEE Trans. Circuits Syst. Video Technol. 33(3), 1320–1334 (2022)
    Article MATH Google Scholar
  26. Liu, X., Gong, W., Shang, L., Li, X., Gong, Z.: Remote sensing image target detection and recognition based on yolov5. Remote Sens. 15(18), 4459 (2023)
    Article MATH Google Scholar
  27. Xie, T., Han, W., Xu, S.: Yolo-rs: a more accurate and faster object detection method for remote sensing images. Remote Sens. 15(15), 3863 (2023)
    Article MATH Google Scholar
  28. Li, Z., Yuan, J., Li, G., Wang, H., Li, X., Li, D., Wang, X.: Rsi-yolo: object detection method for remote sensing images based on improved yolo. Sensors 23(14), 6414 (2023)
    Article MATH Google Scholar
  29. Li, J., Tian, P., Song, R., Xu, H., Li, Y., Du, Q.: Pcvit: a pyramid convolutional vision transformer detector for object detection in remote-sensing imagery. IEEE Trans. Geosci. Remote Sens. 62, 1–15 (2024)
    MATH Google Scholar
  30. Cao, Y., Guo, L., Xiong, F., Kuang, L., Han, X.: Physical-simulation-based dynamic template matching method for remote sensing small object detection. IEEE Trans. Geosci. Remote Sens. 62, 1–14 (2024)
    MATH Google Scholar
  31. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
  32. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11534–11542 (2020)
  33. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision, pp. 3–19 (2018)
  34. Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 13713–13722 (2021)
  35. Song, T., Zhang, X., Yang, D., Ye, Y., Liu, C., Zhou, J., Song, Y.: Lightweight detection network based on receptive-field feature enhancement convolution and three dimensions attention for images captured by UAVS. Image Vis. Comput. 140, 104855 (2023)
    Article MATH Google Scholar
  36. Cui, L., Lv, P., Jiang, X., Gao, Z., Zhou, B., Zhang, L., Shao, L., Xu, M.: Context-aware block net for small object detection. IEEE Trans. Cybern. 52(4), 2300–2313 (2020)
    Article MATH Google Scholar
  37. Sunkara, R., Luo, T.: No more strided convolutions or pooling: A new cnn building block for low-resolution images and small objects. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 443–459 (2022)
  38. 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
  39. Zhang, Y., Yuan, Y., Feng, Y., Lu, X.: Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection. IEEE Trans. Geosci. Remote Sens. 57(8), 5535–5548 (2019)
    Article MATH Google Scholar
  40. Haroon, M., Shahzad, M., Fraz, M.M.: Multisized object detection using spaceborne optical imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 3032–3046 (2020)
    Article MATH Google Scholar
  41. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
  42. Glenn, J.: Yolov5 release v6.0. Github:ultralytics/yolov5 (2022)
  43. Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023)
  44. Glenn, J.: Ultralytics yolov8. Github:ultralytics/yolov8 (2023)
  45. Hu, M., Li, Z., Yu, J., Wan, X., Tan, H., Lin, Z.: Efficient-lightweight yolo: improving small object detection in yolo for aerial images. Sensors 23(14), 6423 (2023)

Download references