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AF-FCOS: An Improved Anchor-Free Object Detection Method

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Applied Intelligence (ICAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2014))

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Abstract

The anchor-free object detection method avoids the complex hyper-parameter setting problem of the traditional anchor-based method, and has more advantages in accuracy and speed. However, it still has some problems such as low precision of small-scale object detection and poor detection result under complex background. To solve this problem, an improved anchor-free object detection method based on FCOS is proposed in this paper. By adding an efficient channel attention mechanism module to the feature extraction network and using a local cross-channel interaction strategy without dimensionality reduction, the 1D convolution kernel size is adaptively selected to obtain useful dependencies between channels and improve feature extraction capability. A context extraction module is designed in feature fusion network to explore context information from multiple receptive fields and improve classification accuracy. In the training stage, DIoU loss is used to make the regression of the border more stable and accurate, and the training process converges faster. The proposed method is evaluated on COCO2017. The experimental results show that compared with the baseline FCOS method, the average accuracy of the proposed method is improved by 1.3%, which has advantages in comprehensive performance.

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References

  1. 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, pp. 6154–6162 (2018)

    Google Scholar 

  2. Cao, J., Chen, Q., Guo, J., Shi, R.: Attention-guided context feature pyramid network for object detection. arXiv preprint arXiv:2005.11475 (2020)

  3. Dai, J., Li, Y., He, K., Sun, J.: R-fcn: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)

    Google Scholar 

  4. De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the crossentropy method. Ann. Oper. Res. 134, 19–67 (2005)

    Article  MathSciNet  Google Scholar 

  5. Dong, Z., Li, G., Liao, Y., Wang, F., Ren, P., Qian, C.: Centripetalnet: pursuing high-quality keypoint pairs for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10519–10528 (2020)

    Google Scholar 

  6. 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)

    Google Scholar 

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

    Google Scholar 

  8. 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 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. Huang, L., Yang, Y., Deng, Y., Yu, Y.: Densebox: unifying landmark localization with end to end object detection. arXiv preprint arXiv:1509.04874 (2015)

  12. Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: beyound anchor-based object detection. IEEE Trans. Image Process. 29, 7389–7398 (2020)

    Article  Google Scholar 

  13. Law, H., Deng, J.: Cornernet: detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018)

    Google Scholar 

  14. Lin, T.Y., Doll´ar, 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)

    Google Scholar 

  15. Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll´ar, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  16. Lin, TY., et al.: Microsoft coco: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision – ECCV 2014. ECCV 2014, LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

  17. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  18. Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., Lin, D.: Libra r-cnn: towards balanced learning for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 821–830 (2019)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)

    Google Scholar 

  21. 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)

    Google Scholar 

  22. Sun, P., et al.: Sparse r-cnn: end-to-end object detection with learnable proposals. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14454–14463 (2021)

    Google Scholar 

  23. 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 

  24. 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/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534–11542 (2020)

    Google Scholar 

  25. Yan, X., Chen, Z., Xu, A., Wang, X., Liang, X., Lin, L.: Meta r-cnn: towards general solver for instance-level low-shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9577–9586 (2019)

    Google Scholar 

  26. Zand, M., Etemad, A., Greenspan, M.: ObjectBox: from centers to boxes for anchor-free object detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision – ECCV 2022. ECCV 2022. LNCS, vol. 13670, pp. 390–406. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20080-9_23

  27. 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)

    Google Scholar 

  28. Zheng, Z., et al.: Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans. Cybern. 52(8), 8574–8586 (2021)

    Article  Google Scholar 

  29. Zhou, X., Koltun, V., Krähenbühl, P.: Probabilistic two-stage detection. arXiv preprint arXiv:2103.07461 (2021)

  30. Zhu, C., He, Y., Savvides, M.: Feature selective anchor-free module for single-shot object detection. In: Proceedings of the IEEE/CVF Conference on Computer vision and Pattern Recognition, pp. 840–849 (2019)

    Google Scholar 

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 62362014, 62172120, and 62002082), and the Guangxi Natural Science Foundation of China (Grant Nos. 2019GXNSFAA245014, 2020GXNSFBA238014).

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Authors and Affiliations

  1. Guangxi Key Laboratory of Image Graphics and Intelligent Processing, Guilin University of Electronic Science and Technology, Guilin, Guangxi, China

    Hang Li, Rui Yang & Rushi Lan

  2. Satellite Navigation Positioning and Location Service National and Local Joint, Engineering Research Center, Guilin, Guangxi, China

    Xiaonan Luo

Authors
  1. Hang Li
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  2. Rui Yang
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  3. Rushi Lan
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  4. Xiaonan Luo
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Corresponding author

Correspondence to Rushi Lan .

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Editors and Affiliations

  1. Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Guangxi Academy of Sciences, Guangxi, China

    Changan Yuan

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, H., Yang, R., Lan, R., Luo, X. (2024). AF-FCOS: An Improved Anchor-Free Object Detection Method. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2014. Springer, Singapore. https://doi.org/10.1007/978-981-97-0903-8_26

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  • DOI: https://doi.org/10.1007/978-981-97-0903-8_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0902-1

  • Online ISBN: 978-981-97-0903-8

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