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Affinity-Aware Relation Network for Oriented Object Detection in Aerial Images

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13845))

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Abstract

Object detection in aerial images is a challenging task due to the oriented and densely packed objects. However, densely packed objects constitute a significant characteristic of aerial images: objects are not randomly scattered around in images but in groups sharing similar orientations. Such a recurring pattern of object arrangement could enhance the rotated features and improve the detection performance. This paper proposes a novel and flexible Affinity-Aware Relation Network based on two-stage detectors. Specifically, an affinity-graph construction module is adopted to measure the affinity among objects and to select bounding boxes sharing high similarity with the reference box. Furthermore, we design a dynamic enhancement module, which uses the attention to learn neighbourhood message and dynamically determines weights for feature enhancement. Finally, we conduct experiments on several public benchmarks and achieve notable AP improvements as well as state-of-the-art performances on DOTA, HRSC2016 and UCAS-AOD datasets.

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References

  1. Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. ar**v preprint ar**v:1312.6203 (2013)

  2. Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: a language modeling framework for object detection. ar**v preprint ar**v:2109.10852 (2021)

  3. Chen, X., Gupta, A.: An implementation of faster RCNN with study for region sampling. ar**v preprint ar**v:1702.02138 (2017)

  4. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural information Processing Systems, vol. 29 (2016)

    Google Scholar 

  5. Ding, J., Xue, N., Long, Y., **a, G.S., Lu, Q.: 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 (2019)

    Google Scholar 

  6. Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263–1272. PMLR (2017)

    Google Scholar 

  7. Guo, Z., Liu, C., Zhang, X., Jiao, J., Ji, X., Ye, Q.: Beyond bounding-box: convex-hull feature adaptation for oriented and densely packed object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8792–8801 (2021)

    Google Scholar 

  8. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  9. Han, J., Ding, J., Li, J., **a, G.S.: Align deep features for oriented object detection. IEEE Trans. Geosci. Remote Sens. 60, 1–11 (2021)

    Google Scholar 

  10. Han, J., Ding, J., Xue, N., **a, G.S.: Redet: a rotation-equivariant detector for aerial object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2786–2795 (2021)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. Hechtlinger, Y., Chakravarti, P., Qin, J.: A generalization of convolutional neural networks to graph-structured data. ar**v preprint ar**v:1704.08165 (2017)

  13. Hou, L., Lu, K., Xue, J., Li, Y.: Shape-adaptive selection and measurement for oriented object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence (2022)

    Google Scholar 

  14. Huang, Z., Li, W., **a, X.G., Tao, R.: A general gaussian heatmap label assignment for arbitrary-oriented object detection. IEEE Trans. Image Process. 31, 1895–1910 (2022)

    Article  Google Scholar 

  15. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ar**v preprint ar**v:1609.02907 (2016)

  16. Klicpera, J., Bojchevski, A., Günnemann, S.: Predict then propagate: graph neural networks meet personalized pagerank. ar**v preprint ar**v:1810.05997 (2018)

  17. Li, W., Chen, Y., Hu, K., Zhu, J.: Oriented reppoints for aerial object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1829–1838 (2022)

    Google Scholar 

  18. Liu, Z., Yuan, L., Weng, L., Yang, Y.: 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 (2017)

    Google Scholar 

  19. Lu, D.: Oskdet: Towards orientation-sensitive keypoint localization for rotated object detection. ar**v preprint ar**v:2104.08697 (2021)

  20. Ming, Q., Miao, L., Zhou, Z., Dong, Y.: CFC-net: a critical feature capturing network for arbitrary-oriented object detection in remote-sensing images. IEEE Trans. Geosci. Remote Sensing 60, 1–14 (2021)

    Article  Google Scholar 

  21. Ming, Q., Miao, L., Zhou, Z., Yang, X., Dong, Y.: Optimization for arbitrary-oriented object detection via representation invariance loss. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2021)

    Article  Google Scholar 

  22. Ming, Q., Zhou, Z., Miao, L., Zhang, H., Li, L.: Dynamic anchor learning for arbitrary-oriented object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2355–2363 (2021)

    Google Scholar 

  23. Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.M.: Geometric deep learning on graphs and manifolds using mixture model CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5115–5124 (2017)

    Google Scholar 

  24. Pan, X., et al.: Dynamic refinement network for oriented and densely packed object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11207–11216 (2020)

    Google Scholar 

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

    Google Scholar 

  26. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. ar**v preprint ar**v:1804.02767 (2018)

  27. Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  28. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61–80 (2008)

    Article  Google Scholar 

  29. 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)

    Google Scholar 

  30. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  31. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. ar**v preprint ar**v:1710.10903 (2017)

  32. Wang, J., Ding, J., Guo, H., Cheng, W., Pan, T., Yang, W.: Mask OBB: a semantic attention-based mask oriented bounding box representation for multi-category object detection in aerial images. Remote Sens. 11(24), 2930 (2019)

    Article  Google Scholar 

  33. Wang, J., Yang, W., Li, H.C., Zhang, H., **a, G.S.: Learning center probability map for detecting objects in aerial images. IEEE Trans. Geosci. Remote Sens. 59(5), 4307–4323 (2020)

    Article  Google Scholar 

  34. Wei, H., Zhang, Y., Zhonghan, C., Li, H., Wang, H., Sun, X.: Oriented objects as pairs of middle lines. ISPRS J. Photogram. Remote Sens. 169, 268–279 (2020). https://doi.org/10.1016/j.isprsjprs.2020.09.022

  35. Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861–6871. PMLR (2019)

    Google Scholar 

  36. **a, G.S., et al.: 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 (2018)

    Google Scholar 

  37. **e, X., Cheng, G., Wang, J., Yao, X., Han, J.: Oriented R-CNN for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3520–3529 (2021)

    Google Scholar 

  38. Xu, Y., et al.: Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1452–1459 (2020)

    Article  Google Scholar 

  39. Yan, S., **ong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  40. Yang, X., Hou, L., Zhou, Y., Wang, W., Yan, J.: Dense label encoding for boundary discontinuity free rotation detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15819–15829 (2021)

    Google Scholar 

  41. Yang, X., Yan, J.: Arbitrary-oriented object detection with circular smooth label. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 677–694. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_40

    Chapter  Google Scholar 

  42. Yang, X., Yan, J., Feng, Z., He, T.: 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 (2021)

    Google Scholar 

  43. Yang, X., Yan, J., Liao, W., Yang, X., Tang, J., He, T.: Scrdet++: detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing. IEEE Trans. Pattern Anal. Mach. Intell. 45, 2384–2399 (2022)

    Article  Google Scholar 

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

    Google Scholar 

  45. Yang, X., et al.: 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 (2019)

    Google Scholar 

  46. Yang, X., et al.: Learning high-precision bounding box for rotated object detection via kullback-leibler divergence. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

    Google Scholar 

  47. Yang, X., et al.: The kfiou loss for rotated object detection. ar**v preprint ar**v:2201.12558 (2022)

  48. Yang, Y., Chen, J., Zhong, X., Deng, Y.: Polygon-to-polygon distance loss for rotated object detection. In: AAAI Conference on Artificial Intelligence (2022)

    Google Scholar 

  49. Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., Metaxas, D.: 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 (2021)

    Google Scholar 

  50. 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)

    Google Scholar 

  51. Zhao, P., Qu, Z., Bu, Y., Tan, W., Guan, Q.: Polardet: a fast, more precise detector for rotated target in aerial images. Int. J. Remote Sens. 42(15), 5831–5861 (2021)

    Article  Google Scholar 

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

    Article  Google Scholar 

  53. 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 (2015)

    Google Scholar 

  54. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: deformable transformers for end-to-end object detection. ar**v preprint ar**v:2010.04159 (2020)

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Acknowledgements

This work is supported by the Natural Resources Science and Technology Project of Anhui Province (Grant No. 2021-K-14).

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Authors and Affiliations

  1. School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China

    Tingting Fang, Bin Liu, Zhiwei Zhao, Qi Chu & Nenghai Yu

  2. Key Laboratory of Electromagnetic Space Information, Chinese Academy of Science, Bei**g, China

    Tingting Fang, Bin Liu, Zhiwei Zhao, Qi Chu & Nenghai Yu

Authors
  1. Tingting Fang
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  2. Bin Liu
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  3. Zhiwei Zhao
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  4. Qi Chu
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  5. Nenghai Yu
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Corresponding author

Correspondence to Bin Liu .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Fang, T., Liu, B., Zhao, Z., Chu, Q., Yu, N. (2023). Affinity-Aware Relation Network for Oriented Object Detection in Aerial Images. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13845. Springer, Cham. https://doi.org/10.1007/978-3-031-26348-4_22

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  • DOI: https://doi.org/10.1007/978-3-031-26348-4_22

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