Smoking-YOLOv8: a novel smoking detection algorithm for chemical plant personnel
Authors: 
Zhong Wang, Yi Liu, Lanfang Lei, Peibei ShiAuthors Info & Claims
Zhong Wang, Yi Liu, Lanfang Lei, Peibei ShiAuthors Info & ClaimsAbstract
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 ([email protected]) by 6.18%, surpassing other mainstream models in overall performance.
References
[1]
Yang J-F et al. Analysis on causes of chemical industry accident from 2015 to 2020 in Chinese mainland: a complex network theory approach J Loss Prev Process Ind 2023 83 105061
[2]
Li X et al. Exploring hazardous chemical explosion accidents with association rules and Bayesian networks Reliab Eng Sys Saf 2023 233 109099
[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. An efficient ResNetSE architecture for smoking activity recognition from smartwatch Intell Autom Soft Comput 2023 35 1 1245-1259
[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. Research on smoking detection based on deep learning J Phys Conf Ser 2021 2024 1 012042
[8]
Senyurek V et al. Cigarette smoking detection with an inertial sensor and a smart lighter Sensors 2019 19 3 570
[9]
Thakur SS et al. Real-time prediction of smoking activity using machine learning based multi-class classification model Multimedia tools and applications 2022 81 10 14529-14551
[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. Smoking behavior detection algorithm based on YOLOv8-MNC Front Comput Neurosci 2023 17 1243779
[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. A smoking behavior detection method based on the YOLOv5 network In J Phys Conf Ser 2022 2232 012001
[19]
Wang Z et al. A smoke detection model based on improved YOLOv5 Mathematics 2022 10 7 1190
[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. Focal and efficient IOU loss for accurate bounding box regression Neurocomputing 2022 506 146-157
[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
Index Terms
- Smoking-YOLOv8: a novel smoking detection algorithm for chemical plant personnel
Index terms have been assigned to the content through auto-classification.
Recommendations
An Intelligent Detection Approach for Smoking Behavior
Smoking in public places not only causes potential harm to the health of oneself and others, but also causes hidden dangers such as fires. Therefore, for health and safety considerations, a detection model is designed based on deep learning for places ...
An Audio-based Hierarchical Smoking Behavior Detection System Based on A Smart Neckband Platform
MOBIQUITOUS 2016: Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and ServicesSmoking behavior detection has attracted much research interest for its significant impact on smokers' physical and mental health. Existing research has shown the potential of using wearable devices for fine-grained smoking puff and session detection by ...
Comments
Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. 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.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 24 June 2024
Accepted: 18 June 2024
Received: 07 March 2024
Author Tags
Qualifiers
- Research-article
Funding Sources
- National Natural Science Foundation of China
- Natural Science Research Key Project for Colleges and University of Anhui Province
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 27 Jan 2025