Skip to main content
Springer Nature Link
Log in

Smoking-YOLOv8: a novel smoking detection algorithm for chemical plant personnel

  • Original Article
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

This study aims to address the challenges of detecting smoking behavior among workers in chemical plant environments. Smoking behavior is difficult to discern in images, with the cigarette occupying only a small pixel area, compounded by the complex background of chemical plants. Traditional models struggle to accurately capture smoking features, leading to feature loss, reduced recognition accuracy, and issues like false positives and missed detections. To overcome these challenges, we have developed a smoking behavior recognition method based on the YOLOv8 model, named Smoking-YOLOv8. Our approach introduces an SD attention mechanism that focuses on the smoking areas within images. By aggregating information from different positions through weighted averaging, it effectively manages long-distance dependencies and suppresses irrelevant background noise, thereby enhancing detection performance. Furthermore, we utilize Wise-IoU as the regression loss for bounding boxes, along with a rational gradient distribution strategy that prioritizes samples of average quality to improve the model’s precision in localization. Finally, the introduction of SPPCSPC and PConv modules in the neck section of the network allows for multi-faceted feature extraction from images, reducing redundant computation and memory access, and effectively extracting spatial features to balance computational load and optimize network architecture. Experimental results on a custom dataset of smoking behavior in chemical plants show that our model outperforms the standard YOLOv8 model in mean Average Precision (mAP@0.5) by 6.18%, surpassing other mainstream models in overall performance.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available in the GitHub repository, https://github.com/hfuuliuyi/smokingYolo/tree/master.

Code availability

The source code of the performed experiments are available in the GitHub repository, https://github.com/hfuuliuyi/smokingYolo.

References

  1. Yang J-F et al (2023) Analysis on causes of chemical industry accident from 2015 to 2020 in Chinese mainland: a complex network theory approach. J Loss Prev Process Ind 83:105061

    Article  Google Scholar 

  2. Li X et al (2023) Exploring hazardous chemical explosion accidents with association rules and Bayesian networks. Reliab Eng Sys Saf 233:109099

    Article  Google Scholar 

  3. Zheng X et al (2016) Smokey: ubiquitous smoking detection with commercial WiFi infrastructures. In: IEEE INFOCOM 2016-The 35th annual IEEE international conference on computer communications. IEEE

  4. Hnoohom N et al (2023) An efficient ResNetSE architecture for smoking activity recognition from smartwatch. Intell Autom Soft Comput 35(1):1245–1259

    Article  Google Scholar 

  5. Cui C and R Xu (2022) Multiple machine learning algorithms for human smoking behavior detection. In: 2022 international conference on Machine Learning and Intelligent Systems Engineering (MLISE). IEEE.

  6. Zhang D et al (2018) Smoking image detection based on convolutional neural networks. In: 2018 IEEE 4th International Conference on Computer and Communications (ICCC). IEEE

  7. Zhang Z et al (2021) Research on smoking detection based on deep learning. J Phys Conf Ser 2024(1):012042

    Article  Google Scholar 

  8. Senyurek V et al (2019) Cigarette smoking detection with an inertial sensor and a smart lighter. Sensors 19(3):570

    Article  Google Scholar 

  9. Thakur SS et al (2022) Real-time prediction of smoking activity using machine learning based multi-class classification model. Multimedia tools and applications 81(10):14529–14551

    Article  Google Scholar 

  10. Viola P and M Jones (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001. IEEE

  11. Dalal N and B Triggs (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05). IEEE

  12. Felzenszwalb P et al. (2008) A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE conference on computer vision and pattern recognition. IEEE

  13. Rentao Z et al. (2019) Indoor smoking behavior detection based on yolov3-tiny. In: 2019 Chinese Automation Congress (CAC). IEEE

  14. Adebowale MA et al. (2019) Deep learning with convolutional neural network and long short-term memory for phishing detection. In: 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). IEEE

  15. Ma Y et al. (2022) YOLO-cigarette: An effective YOLO network for outdoor smoking real-time object detection. In: 2021 Ninth International Conference on Advanced Cloud and Big Data (CBD). IEEE

  16. Wang ZL et al (2023) Smoking behavior detection algorithm based on YOLOv8-MNC. Front Comput Neurosci 17:1243779

    Article  Google Scholar 

  17. Liao J and J Zou (2020) Smoking target detection based on Yolo V3. In: 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). IEEE

  18. Jiang X et al (2022) A smoking behavior detection method based on the YOLOv5 network. In J Phys Conf Ser 2232:012001

    Article  Google Scholar 

  19. Wang Z et al (2022) A smoke detection model based on improved YOLOv5. Mathematics 10(7):1190

    Article  Google Scholar 

  20. Chiu C-F et al (2018) Smoking action recognition based on spatial-temporal convolutional neural networks. In: 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE

  21. Li C, Li L, Jiang H, et al (2022) YOLOv6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976.

  22. Wang C-Y (2023) YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

  23. Ge Z, Liu S, Wang F, et al (2021) Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430

  24. Bochkovskiy A, Wang CY, Liao H YM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934

  25. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25

  26. Hu J et al (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  27. Wang Q et al (2020) ECA-Net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

  28. Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Adv Neural Inf Process 30

  29. Dosovitskiy A, Beyer L, Kolesnikov A et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929

  30. Zheng Z et al (2020) Distance-IoU loss: faster and better learning for bounding box regression. In: Proceedings of the AAAI conference on artificial intelligence

  31. Zhang Y-F et al (2022) Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506:146–157

    Article  Google Scholar 

  32. Gevorgyan Z (2022) SIoU loss: more powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740

  33. Tong Z, Chen Y, Xu Z, et al (2023) Wise-IoU: bounding box regression loss with dynamic focusing mechanism. arXiv preprint arXiv:2301.10051

  34. Howard A G, Zhu M, Chen B, et al (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

  35. Zhang X et al (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  36. Han K et al (2020) Ghostnet: More features from cheap operations. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

  37. Chen J et al (2023) Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

  38. Rezatofighi H et al (2019) 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

  39. Siliang M, Yong X (2023) MPDIoU: A loss for efficient and accurate bounding box regression. arXiv preprint arXiv:2307.07662

  40. Yang L et al (2021) Simam: A simple, parameter-free attention module for convolutional neural networks. In: International conference on machine learning. PMLR

  41. Misra D et al (2021) Rotate to attend: Convolutional triplet attention module. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision

  42. Zhang Q-L and 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

  43. Woo S et al (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV)

  44. Zhu L et al (2023) BiFormer: Vision Transformer with Bi-Level Routing Attention. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

  45. Li X et al (2019) Expectation-maximization attention networks for semantic segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision

  46. Liu Y, Shao Z, Hoffmann N (2021) Global attention mechanism: retain information to enhance channel-spatial interactions. arXiv preprint arXiv:2112.05561

  47. Hou Q et al (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61976198), and the Natural Science Research Key Project for Colleges and University of Anhui Province (Grant No. 2022AH052141 and 2022AH052142), and the 2023 Humanities and Social Science General Program sponsored by the Ministry of Education of the People’s Republic of China (Grant No. 23YJCZH067), and the Hefei Municipal Natural Science Foundation (Grant No. 202322).

Funding

National Natural Science Foundation of China, 61976198, Natural Science Research Key Project for Colleges and University of Anhui Province, 2022AH052141, 2022AH052141, 2022AH052141, 2022AH052141

Author information

Authors and Affiliations

  1. School of Artificial Intelligence and Big Data, Hefei University, Hefei, 230601, China

    Zhong Wang, Yi Liu & Lanfang Lei

  2. School of Computer and Artifcial Intelligence, Hefei Normal University, Hefei, 230601, China

    Zhong Wang & Peibei Shi

  3. Hefei Institute for Public Safety Research, Tsinghua University, Hefei, 230601, China

    Zhong Wang

Authors
  1. Zhong Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yi Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Lanfang Lei
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Peibei Shi
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Zhong Wang was contributed to supervision, project administration, writing—review and editing. YI Liu was contributed to conceptualization, methodology, software, validation, writing—original draft. Lanfang Lei was contributed to writing—review and editing. Peibei Shi was contributed to writing resources, formal analysis.

Corresponding author

Correspondence to Zhong Wang.

Ethics declarations

Conflicts of interest

The authors declare no competing interests.

Ethical approval

Written informed consent for publication of this paper was obtained from all authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Liu, Y., Lei, L. et al. Smoking-YOLOv8: a novel smoking detection algorithm for chemical plant personnel. Pattern Anal Applic 27, 72 (2024). https://doi.org/10.1007/s10044-024-01288-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10044-024-01288-7

Keywords