Skip to main content
SpringerLink
Log in

Progressive Fusion Network for Safety Protection Detection

  • Conference paper
  • First Online:
Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12888))

Included in the following conference series:

  • 1906 Accesses

Abstract

In recent years, it leads to the occurrence of many accidents and huge economic losses, because the construction personnel do not wear safety protective equipment normatively. Therefore, safety protection detection becomes an important problem in the computer vision community. It is a challenging problem because the targets are usually very small, the background is usually very complex at construction site image. To solve these problems, we propose a progressive fusion network PFNet. In PFNet, we use a progressive fusion module to enrich semantic information and a feature enhancement module to enhance detailed information in feature learning. Therefore, we can obtain effective features for safety protection detection. To provide an evaluation platform, we create an image dataset, with 5430 images and careful annotations for safety protection detection. PFNet achieves detection accuracy of 63.7% mAP in our dataset, which is 3.6% higher than the baseline method. PFNet also achieves great detection performance on other datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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. Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. arXiv preprint arXiv:1605.06409 (2016)

  3. Di Benedetto, M., Carrara, F., Meloni, E., Amato, G., Falchi, F., Gennaro, C.: Learning accurate personal protective equipment detection from virtual worlds. Multimedia Tools Appl. 80(15), 23241–23253 (2020). https://doi.org/10.1007/s11042-020-09597-9

    Article  Google Scholar 

  4. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

  5. Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)

    Google Scholar 

  6. Ghiasi, G., Lin, T.Y., Le, Q.V.: 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 (2019)

    Google Scholar 

  7. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)

    Google Scholar 

  8. Kurnaz, F.C., Hocaog̃lu, B., Yılmaz, M.K., Sülo, İ, Kalkan, S.: ALET (Automated Labeling of Equipment and Tools): a dataset for tool detection and human worker safety detection. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12538, pp. 371–386. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66823-5_22

    Chapter  Google Scholar 

  9. 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 

  10. Li, Y., Chen, Y., Wang, N., Zhang, Z.: Scale-aware trident networks for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6054–6063 (2019)

    Google Scholar 

  11. Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: DetNet: a backbone network for object detection. arXiv preprint arXiv:1804.06215 (2018)

  12. 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, pp. 2117–2125 (2017)

    Google Scholar 

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

    Google Scholar 

  14. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)

    Google Scholar 

  15. Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  16. Liu, Y., et al.: CBNet: a novel composite backbone network architecture for object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11653–11660 (2020)

    Google Scholar 

  17. Long, X., Cui, W., Zheng, Z.: Safety helmet wearing detection based on deep learning. In: 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 2495–2499. IEEE (2019)

    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. Qiao, S., Chen, L.C., Yuille, A.: DetectoRS: detecting objects with recursive feature pyramid and switchable atrous convolution. arXiv preprint arXiv:2006.02334 (2020)

  20. 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 

  21. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  22. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497 (2015)

  23. Singh, B., Najibi, M., Davis, L.S.: SNIPER: efficient multi-scale training. arXiv preprint arXiv:1805.09300 (2018)

  24. 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 

  25. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)

  26. Zhang, H., Chang, H., Ma, B., Wang, N., Chen, X.: Dynamic R-CNN: towards high quality object detection via dynamic training. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12360, pp. 260–275. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58555-6_16

    Chapter  Google Scholar 

  27. 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 

  28. 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 

  29. Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)

Download references

Acknowledgment

This work was supported by University Synergy Innovation Program of Anhui Province (No. GXXT-2019-007), the National Natural Science Foundation of China (No. 62076003), Anhui Provincial Natural Science Foundation (No. 1908085MF206).

Author information

Authors and Affiliations

  1. Anhui University, Hefei, 230601, China

    Futian Wang, Lugang Wang, Jin Tang & Chenglong Li

Authors
  1. Futian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lugang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenglong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chenglong Li .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, F., Wang, L., Tang, J., Li, C. (2021). Progressive Fusion Network for Safety Protection Detection. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87355-4_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87354-7

  • Online ISBN: 978-3-030-87355-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation