Abstract
Current rotating object detection task achieves good results based on large models. In order to reduce the size of model, we propose a lightweight network SFC (ShuffleNet combines FPN with CSL) for rotating target detection. SFC first introduces circular smooth label (CSL) to detect target rotations, which transforms the traditional angle regression problem into classification problem. Then, the lightweight ShuffleNetV2 is utilized as the backbone to reduce the number of parameters. ShuffleNetV2 is used for feature extraction, and CSL is introduced to address the periodic problem of angles. Comparative experiments were carried out on DOTA 1.5 dataset. The experimental results show that the proposed method reduces the parameter by nearly 90% with a slight loss of accuracy, and increases the inferencing speed by 40% at the same time.
Y. Jiao and Q. Zhu—Contributed equally to this work.
This work is partially supported by the National Natural Science Foundation of China (62101552) and by the Key R &D Program of the Chinese Academy of Sciences (ZDRW-XH-2021-3-03).
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References
Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988 (2017)
Iandola, F.N., et al.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)
Howard, A.G., et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_8
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
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 (CVPR), pp. 2117–2125 (2017)
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 (ICML), pp. 11830–11841. PMLR (2021)
Ding, J., Xue, N., Long, Y., Xia, G-.S., Lu, O.: Learning roi transformer for oriented object detection in aerial images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2849–2858 (2019)
Zhang, G., Shijian, L., Zhang, W.: Cad-net: a context-aware detection network for objects in remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 57(12), 10015–10024 (2019)
Yang, X., Yan, J., Feng, Z., He, T.: R3det: refined single-stage detector with feature refinement for rotating object. In Proc. AAAI Conf. Artif. Intell. 35, 3163–3171 (2021)
Li, Z., Hou, B., Wu, Z., Jiao, L., Ren, B., Yang, C.: Fcosr: A simple anchor-free rotated detector for aerial object detection. arXiv preprint arXiv:2111.10780 (2021)
Zhang, F., Wang, X., Zhou, S., Wang, Y.: DARDet: A dense anchor-free rotated object detector in aerial images. IEEE Geosci. Remote Sensing Lett. 19 1–5 (2021)
Ding, J., Xue, N., Long, Y., Xia, G.-S., Lu, Q.: Learning roi transformer for detecting oriented objects in aerial images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 31 (2018)
Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-iou loss: faster and better learning for bounding box regression. In Proc. AAAI Conf. Artif. Intell. 34, 12993–13000 (2020)
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 (CVPR), pp .658–666 (2019)
Xia, G.-.S.: Dota: A large-scale dataset for object detection in aerial images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Ming, Q., Miao, L., Zhou, Z., Yang, X., Dong, Y.: Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss. In: IEEE Geoscience and Remote Sensing Letters, pp. 1–5 (2021)
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, pp. 2458–2466 (2021)
Qian, W., Yang, X., Peng, S., Zhang, X., Yan, J.: RSDet++: Point-based modulated loss for more accurate rotated object detection. In: IEEE Transactions on Circuits and Systems for Video Technology (2022)
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)
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Jiao, Y., Zhu, Q., He, H., Zhao, T., Wang, H. (2022). Rotating Target Detection Based on Lightweight Network. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13631. Springer, Cham. https://doi.org/10.1007/978-3-031-20868-3_46
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