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SAFPN: a full semantic feature pyramid network for object detection

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Abstract

To enhance the performance of object detection algorithm, this paper proposes segmentation attention feature pyramid network (SAFPN) to address the issue of semantic information loss. Compared to prior works, SAFPN discards the original \(1\times 1\) convolutions and achieves feature dimension reduction through a segmentation and accumulation architecture, thereby preserving the semantic information of high-dimensional features completely. To capture fine-grained semantic details, it integrates channel attention and spatial attention mechanisms to enhance the network’s focus on important information. Extensive experimental validation demonstrates that SAFPN achieves favorable results on multiple public datasets, and can better complete the target detection task.

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Data availability statements

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Zhang L, Wang H, Wang X, Liu Q, Wang H, Wang H (2021) Vehicle object detection method based on candidate region aggregation. Pattern Anal Appl 24:1635–1647

    Article  Google Scholar 

  2. Sugiura M, Miyauchi CM, Kotozaki Y, Akimoto Y, Nozawa T, Yomogida Y, Hanawa S, Yamamoto Y, Sakuma A, Nakagawa S et al (2015) Neural mechanism for mirrored self-face recognition. Cereb Cortex 25(9):2806–2814

    Article  Google Scholar 

  3. Yan K, Wang X, Lu L, Summers RM (2018) Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J Med Imaging 5(3):036501–036501

    Article  Google Scholar 

  4. 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

  5. Girshick R (2015) Fast r-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448

  6. Ren S, He K, Girshick R, Sun J (2015) Faster r-CNN: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems

  7. Zhang H, Chang H, Ma B, Wang N, Chen X (2020) Dynamic r-CNN: towards high quality object detection via dynamic training. In: Computer vision—ECCV 2020: 16th European conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16. Springer, Berlin, pp 260–275

  8. Sun P, Zhang R, Jiang Y, Kong T, Xu C, Zhan W, Tomizuka M, Li L, Yuan Z, Wang C et al (2021) 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

  9. 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

  10. Tian Z, Shen C, Chen H, He T (2019) FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 9627–9636

  11. Tian Z, Shen C, Chen H, He T (2019) FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 9627–9636

  12. Zhang S, Chi C, Yao Y, Lei Z, Li SZ (2020) 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

  13. Zhang H, Wang Y, Dayoub F, Sunderhauf N (2021) Varifocalnet: an IOU-aware dense object detector. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8514–8523

  14. Chen Q, Wang Y, Yang T, Zhang X, Cheng J, Sun J (2021) You only look one-level feature. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 13039–13048

  15. Feng C, Zhong Y, Gao Y, Scott MR, Huang W (2021) TOOD: task-aligned one-stage object detection. In: 2021 IEEE/CVF international conference on computer vision (ICCV). IEEE Computer Society, pp 3490–3499

  16. Li S, He C, Li R, Zhang L (2022) A dual weighting label assignment scheme for object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9387–9396

  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. Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759–8768

  19. Liu S, Huang D, Wang Y (2019) Learning spatial fusion for single-shot object detection. arXiv preprint arXiv:1911.09516

  20. Tan M, Pang R, Le QV (2020) Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10781–10790

  21. Qiao S, Chen L-C, Yuille A (2021) Detectors: detecting objects with recursive feature pyramid and switchable atrous convolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10213–10224

  22. Wang G, Gan X, Cao Q, Zhai Q (2022) MFANet: multi-scale feature fusion network with attention mechanism. Visual Comput. https://doi.org/10.1007/s00371-022-02503-4

    Article  Google Scholar 

  23. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  24. Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19

  25. Rahman MM, Fiaz M, Jung SK (2020) Efficient visual tracking with stacked channel-spatial attention learning. IEEE Access 8:100857–100869

    Article  Google Scholar 

  26. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) 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

  27. Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 13713–13722

  28. Yang L, Zhang R-Y, Li L, Xie X (2021) SIMAM: a simple, parameter-free attention module for convolutional neural networks. In: International conference on machine learning, pp 11863–11874. PMLR

  29. Zhang Q-L, Yang Y-B (2021) Sa-net: shuffle attention for deep convolutional neural networks. In: ICASSP 2021-2021 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2235–2239

  30. Mou L, Zhao Y, Chen L, Cheng J, Gu Z, Hao H, Qi H, Zheng Y, Frangi A, Liu J (2019) CS-NET: channel and spatial attention network for curvilinear structure segmentation. In: Medical Image Computing and Computer Assisted Intervention—MICCAI 2019: 22nd international conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part I 22. Springer, pp 721–730

  31. Hsyu M-C, Liu C-W, Chen C-H, Chen C-W, Tsai W-C (2021) CSANET: high speed channel spatial attention network for mobile ISP. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2486–2493

  32. Li H, Xiong P, An J, Wang L (2018) Pyramid attention network for semantic segmentation. arXiv preprint arXiv:1805.10180

  33. Liu Z, Gong P, Wang J (2019) Attention-based feature pyramid network for object detection. In: Proceedings of the 2019 8th international conference on computing and pattern recognition, pp 117–121

  34. Guo C, Fan B, Zhang Q, Xiang S, Pan C (2020) AUGFPN: improving multi-scale feature learning for object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12595–12604

  35. Min K, Lee G-H, Lee S-W (2022) Attentional feature pyramid network for small object detection. Neural Netw 155:439–450

    Article  Google Scholar 

  36. Yang X, Wang W, Wu J, Ding C, Ma S, Hou Z (2022) MLA-NET: feature pyramid network with multi-level local attention for object detection. Mathematics 10(24):4789

    Article  Google Scholar 

  37. Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part V 13. Springer, Berlin, pp 740–755

  38. Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vision 88:303–338

    Article  Google Scholar 

  39. Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vis 111:98–136

    Article  Google Scholar 

  40. Zhang H, Li D, Ji Y, Zhou H, Wu W (2019) Deep learning-based beverage recognition for unmanned vending machines: an empirical study. In: 2019 IEEE 17th international conference on industrial informatics (INDIN). IEEE, vol 1, pp 1464–1467

  41. Zhang H, Li D, Ji Y, Zhou H, Wu W, Liu K (2019) Toward new retail: a benchmark dataset for smart unmanned vending machines. IEEE Trans Ind Inf 16(12):7722–7731

    Article  Google Scholar 

  42. Chen K, Wang J, Pang J, Cao Y, Xiong Y, Li X, Sun S, Feng W, Liu Z, Xu J, et al (2019) Mmdetection: open MMLAB detection toolbox and benchmark. arXiv preprint arXiv:1906.07155

  43. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

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Funding

This work was supported by the National Key R &D Program of China (Grant numbers [2017YFB1302400]).

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Author notes

  1. Nengyuan Wang and Hong Liu have contributed equally to this work.

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, 430068, China

    Gaihua Wang, Qi Li, Nengyuan Wang & Hong Liu

  2. Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, 430068, China

    Gaihua Wang

Authors
  1. Gaihua Wang
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  2. Qi Li
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  3. Nengyuan Wang
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  4. Hong Liu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [Gaihua Wang], [Nengyuan Wang], and [Hong Liu]. The first draft of the manuscript was written by [Qi Li] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Qi Li.

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Wang, G., Li, Q., Wang, N. et al. SAFPN: a full semantic feature pyramid network for object detection. Pattern Anal Applic 26, 1729–1739 (2023). https://doi.org/10.1007/s10044-023-01200-9

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