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Inception-Det: large aspect ratio rotating object detector for remote sensing images

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Abstract

One important area of research in the field of remote sensing information processing is image object detection. Finding expected objects in an image and returning their category confidence and bounding boxes is the objective of the visual object detection task. Remote sensing images, in contrast to conventional images, contain many objects with large aspect ratios and are densely distributed, posing numerous challenging issues. In this paper, we propose Inception-Det, which makes use of a two-stage detection head design to solve these issues. The first stage is used to predict rotational anchors close to the GT-Box, and the second stage uses a higher positive IoU threshold and complete features calculated by FPN Inception to get better detection results. By effectively resolving the issue of objects with large aspect ratios being difficult to regress to high-precision bounding boxes, our proposed feature-complete transform (FCT) can effectively extend the detailed information contained in the feature map without introducing background noise. Extensive testing on two publicly available datasets, DOTA and HRSC2016, demonstrates that our proposed method outperforms the alternatives and further enhances detection performance at a rapid rate.

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References

  1. 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).

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

  3. 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.

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

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

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

  8. Zhang, S., He, G., Chen, H.-B., Jing, N., Wang, Q. (2019). Scale Adaptive Proposal Network for Object Detection in Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters, 16(6), 864–868.

  9. Qian, X., Lin, S., Cheng, G., Yao, X., Ren, H., Wang, W. (2020). Object detection in remote sensing images based on improved bounding box regression and multi-level features fusion. Remote Sensing, 12(1), 143.

  10. Sun, X., Wang, P., Wang, C.,Liu, y.,Fu, k. (2021). Pbnet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 50–65.

  11. Chen, J., Wan, L., Zhu, J.,Xu, G., Deng, M. (2019). Multi-scale spatial and channel-wise attention for improving object detection in remote sensing imagery. IEEE Geoscience and Remote Sensing Letters, 17(4), 681–685.

  12. Rabbi, J., Ray, N., Schubert, M., Chowdhury, S., Chao, D. (2020). Small-object detection in remote sensing images with end-to-end edge-enhanced gan and object detector network. Remote Sensing, 12(9), 1432.

  13. Ma, J., Shao, W., Ye, H., Wang, L., Wang, H., Zhen, Y., Xue, X. (2018). Arbitrary-oriented scene text detection via rotation proposals. IEEE Transactions on Multimedia, 20(11), 3111–3122.

  14. Ding, J., Xue, N., Long, Y., Xia, G.-S., Lu, Q. (2019) .Learning roi transformer for oriented object detection in aerial images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (pp. 2849–2858).

  15. Zhou, L., Wei, H., Li, H., Zhao, W., Zhao, W.,Zhang, Y., Zhang, Y. (2020). Arbitrary-oriented object detection in remote sensing images based on polar coordinates. IEEE Access, 8, 223373–223384.

  16. Yang, X.,Yan, J., Ming, Q., Wang, W., Zhang, X., Tian, Q. (2021). Rethinking rotated object detection with gaussian wasserstein distance loss. In: International Conference on Machine Learning, (pp. 11830–11841) .PMLR

  17. Lin, T.-Y.,Dollár, P.,Girshick, R., He, K., Hariharan, B., Belongie, S. (2017). Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (pp. 2117–2125).

  18. Ghiasi, G., Lin, T.-Y., Le, Q.V. (2019). Nas-fpn: Learning scalable feature pyramid architecture for object detection.In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (pp.7036–7045).

  19. Tan, M., Pang, R., Le, Q.V. (2020). Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (pp. 10781–10790).

  20. Zhang, T., Zhang, X., Ke, X. (2021). Quad-fpn: A novel quad feature pyramid network for sar ship detection. Remote Sensing, 13(14), 2771.

  21. Gong, Y., Yu, X., Ding, Y., Peng, X., Peng, X., Zhao, J., Han, Z. (2021). Effective fusion factor in fpn for tiny object detection In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision,(pp. 1160–1168).

  22. Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., 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).

  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. Cai, Z., Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,pp. 6154–6162 .

  25. Yang, X., Yan, J., Feng, Z., He, T. (2021). R3det: Refined single-stage detector with feature refinement for rotating object. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.35, (pp.3163–3171) .

  26. Liu, Z., Yuan, L., Weng, L., Yang, Y. (2017). A high resolution optical satellite image dataset for ship recognition and some new baselines. In: International Conference on Pattern Recognition Applications and Methods,vol. 2, (pp. 324–331). SciTePress

  27. Chen, Z., Chen, K., Lin, W., See, J., Yu, H., Ke, Y., Yang, C. (2020). Piou loss: Towards accurate oriented object detection in complex environments. In: European Conference on Computer Vision, (pp. 195–211) .Springer

  28. Xiao, Z., Qian, L., Shao, W., Tan, X., Wang, K. (2020). Axis learning for orientated objects detection in aerial images. Remote Sensing, 12(6), 908.

  29. Azimi, S.M., Vig, E., Bahmanyar, R., Körner, M., Reinartz, P. (2018). Towards multi-class object detection in unconstrained remote sensing imagery.In:Asian Conference on Computer Vision, (pp. 150–165). Springer

  30. Llerena, J.M., Zeni, L.F., Kristen, L.N., Jung, C. (2021). Gaussian bounding boxes and probabilistic intersection-over-union for object detection. arXiv preprint arXiv:2106.06072

  31. Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., Fu, K. (2019). Scrdet: Towards more robust detection for small, cluttered and rotated objects.In:Proceedings of the IEEE/CVF International Conference on Computer Vision,(pp. 8232–8241).

  32. Wei, H., Zhang, Y., Chang, Z., Li, H., Wang, H., Sun, X. (2020). Oriented objects as pairs of middle lines. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 268–279.

  33. Pan, X., Ren, Y., Sheng, K., Dong, W., Yuan,H., Guo, X., Ma, C., Xu, C. (2020). Dynamic refinement network for oriented and densely packed objectdetection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (pp. 11207–11216).

  34. Ming, Q., Miao, L., Zhou, Z.,Dong, Y. (2021). Cfc-net: A critical feature capturing network for arbitrary-oriented object detection in remote-sensing images.IEEE Transactions on Geoscience and Remote Sensing.

  35. Qian, W., Yang, X., Peng, S., Yan, J., Guo, Y. (2021). Learning modulated loss for rotated object detection. In:Proceedings of the AAAI Conference on Artificial Intelligence,vol. 35, (pp.2458–2466).

  36. Xu, Y., Fu, M., Wang, Q., Wang, Y., Wang, Y., Chen, K., Xia, G.-S., Bai, X. (2020). Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE transactions on pattern analysis and machine intelligence, 43(4), 1452–1459.

  37. Kim, B., Lee, J., Lee, S., Kim, D., Kim, J. (2022). Tricubenet: 2d kernel-based object representation for weakly-occluded oriented object detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 167–176 .

  38. Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., Metaxas, D. (2021). Oriented object detection in aerial images with box boundary-aware vectors. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, (pp. 2150–2159).

  39. Zhu, Y., Du, J., Wu, X. (2020). Adaptive period embedding for representing oriented objects in aerialimages. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7247–7257.

  40. Liu, S., Zhang, L., Hao, S., Lu, H., He, Y. (2021) . Polar ray: A single-stage angle-free detector for oriented object detection in aerial images. In: Proceedings of the 29th ACM International Conference on Multimedia,(pp. 3124–3132 ).

  41. Jiang, Y., Zhu, X., Wang, X., Li, W., Wangs, H., Fu, P., Luo, Z. (2017). R2cnn: Rotational region cnn for orientation robust scene text detection. arXiv preprint arXiv:1706.09579

  42. Song, Q., Yang, F., Yang, L., Liu, C., Hu, M., Xia, L. (2020). Learning point-guided localization for detection in remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 1084–1094.

  43. Ming, Q., Miao, L., Zhou, Z., Song, J., Yang, X. (2021). Sparse label assignment for oriented object detection in aerialimages. Remote Sensing,13(14), 2664.

  44. Yang, X., Yan, J., He, T. (2020) . On the arbitrary-oriented object detection: Classification based approaches revisited. arXiv preprint arXiv:2003.05597

  45. Ming, Q., Miao, L., Zhou, Z., Yang, X., Dong, Y. (2021). Optimization for arbitrary-oriented object detection via representation invariance loss. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

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Funding

This work was funded in part by the National Natural Science Foundation of China (Grant Number 62071157), in part by the National key research and development Program (Grant Number 2022YFD2000500), and in part by the Natural Science Foundation of Heilongjiang Province (Grant Number YQ2019F011).

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin University of Science and Technology, No. 52 Xuefu Road, Harbin, 150080, Heilongjiang Province, China

    Ao Li, Yutong Niu, Zening Wang, Zhiwei Liu & Hailu Yang

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  1. Ao Li
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  2. Yutong Niu
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  3. Zening Wang
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  4. Zhiwei Liu
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Correspondence to Zhiwei Liu.

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Li, A., Niu, Y., Wang, Z. et al. Inception-Det: large aspect ratio rotating object detector for remote sensing images. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03253-4

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