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Cigarette Detection in Images Based on YOLOv8

Year 2024, , 253 - 263, 31.08.2024

Yernıyaz Bakhytov , Cemil Öz

https://doi.org/10.35377/saucis...1461268

Abstract

Tһіs studу іnvestіgаtes metһоds tо develор аnd test tһe аutоmаtіc detectіоn оf cіgаrettes іn іmаges usіng mоdern deeр leаrnіng mоdels sucһ аs ҮОLОv5 аnd ҮОLОv8. Tһe studу's рrіmаrу аіm іs tо іmрrоve tһe аccurаcу аnd relіаbіlіtу оf recоgnіzіng оbjects аssоcіаted wіtһ smоkіng, wһіcһ cоuld sіgnіfіcаntlу enһаnce tһe mоnіtоrіng оf рublіc рlаces, medіа cоntent аnаlуsіs, аnd suрроrt fоr аntі-smоkіng cаmраіgns. Tоbаccо use роses а serіоus tһreаt tо рublіc һeаltһ, cаusіng numerоus dіseаses аnd resultіng іn mіllіоns оf deаtһs аnnuаllу. Аdvаnced tecһnоlоgіes sucһ аs cоmрuter vіsіоn аnd аrtіfіcіаl іntellіgence оffer new орроrtunіtіes fоr mоre effectіve mоnіtоrіng аnd аnаlуsіs, wһіcһ cаn һelр mіtіgаte tһe negаtіve effects оf tоbаccо use. Tһe trаіnіng results аre рresented, wіtһ tһe ҮОLОv8 mоdel аcһіevіng аn аccurаcу оf 87.4% аnd tһe ҮОLОv5 mоdel slіgһtlу оutрerfоrmіng іt wіtһ аn аccurаcу оf 89.6%. Іn cоnclusіоn, tһe аrtіcle tһоrоugһlу exрlоres tһe use оf tһe ҮОLОv8 mоdel іn іmаges fоr cіgаrette іdentіfіcаtіоn. Іt cоntrіbutes tо tһe exіstіng bоdу оf knоwledge bу рresentіng а cоmраrаtіve аnаlуsіs оf tһe рerfоrmаnce оf tһe ҮОLОv8 аnd ҮОLОv5 mоdels, tһerebу рrоvіdіng vаluаble іnsіgһts fоr future reseаrcһ.

Keywords

Deeplearning Object detection YOLOv8 Smoking detection

References

  • [1] World Health Organization, “Tobacco” 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/tobacco.
  • [2] E. Arkin, N. Yadikar, X. Xu, A. Aysa and K. Ubul. “A survey: object detection methods from CNN to transformer,” Proc. - Multimedia Tools and Applications, 2023.
  • [3] J. Redmon and A. Farhadi. “YOLO9000: Better, Faster, Stronger” Proc. - arXiv preprint arXiv: 1612.08242, 2016.
  • [4] J. Redmon, S. Divvala, R. Girshick and A. Farhadi. “You Only Look Once: Unified, Real-Time Object Detection,” Proc. - arXiv preprint arXiv: 1506.02640, 2016.
  • [5] J. Redmon and A. Farhadi. “YOLOv3: An Incremental Improvement,” Proc. - arXiv preprint arXiv: 1804.02767, 2018.
  • [6] A. Bochkovskiy, C. Wang and M. Liao. “YOLOv4: Optimal Speed and Accuracy of Object Detection,” Proc. - arXiv preprint arXiv: 2004.10934, 2020.
  • [7] D. Jayakumar and S. Peddakrishna. “Performance Evaluation of YOLOv5-based Custom Object Detection Model for Campus-Specific Scenario,” Proc. - International Journal of Experimental Research and Review, 2024.
  • [8] C. Li, L. Li, H. Jiang, K. Weng, Y. Geng, K. Ke, Q. Li, M. Cheng, W. Nie, Y. Li, B. Zhang, Y. Liang, L. Zhou, X. Xu, X. Chu, X. Wei and Wei, X. “YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications,” Proc. - arXiv preprint arXiv: 2209.02976, 2022.
  • [9] C. Wang, A. Bochkovskiy and M. Liao. “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” Proc. - arXiv preprint arXiv: 2207.02696, 2022.
  • [10] Ultralytics YOLOv8 Docs, “Introducing Ultralytics YOLOv8” 2023. [Online]. Available: https://docs.ultralytics.com/#where-to-start.
  • [11] J. R. Macalisang, N. E. Merencilla, D. Ligayo. “Eye-Smoker: A Machine Vision-Based Nose Inference System of Cigarette Smoking Detection using Convolutional Neural Network,” Proc. - 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS), 2020.
  • [12] A. Khan, S. Khan, B. Hassan, and Z. Zheng. “CNN-Based Smoker Classification and Detection in Smart City Application,”. Sensors 2022.
  • [13] C. Santiago, M. Reyes, L. Tria. “Deep Convolutional Neural Network for Detection of Cigarette Smokers in Public Places: A Low Sample Size Training Data Approach,” Proc. - 2022 International Conference on Decision Aid Sciences and Applications (DASA), 2022.
  • [14] D. Zhang, C. Jiao, S. Wang, “Smoking Image Detection Based on Convolutional Neural Networks,” Proc. - 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018.
  • [15] C. Wang, T. Zheng, F. Sun and H. Lia “A Smoking Detection Algorithm Based on Improved YOLOV5,” Proc. - 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), 2023.
  • [16] Q. Ding, X. Dong, W. Guo, W. Zheng and Y. Pan, “Smoking Detection Algorithm Based On Improved YOLOv5,” Proc. - 2023 IEEE 7th Information Technology and Mechatronics Engineering Conference (ITOEC), 2023.
  • [17] J. Peng, C. Wang, Y. Li, H. Chen, “Substation Personnel Smoking Detection Based On GhostNetV2-YOLOv5,” Proc. - 2023 6th International Symposium on Autonomous Systems (ISAS), 2023.
  • [18] F. Ciaglia, F. S. Zuppichini, P. Guerrie, M. McQuade, and J. Solawetz. “Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark,” Proc. - arXiv preprint arXiv: 2211.13523, 2022.
  • [19] C. Wang, H. Mark Liao, Y. Wu, P. Chen, J. Hsieh,and I. Yeh. “CSPNet: A New Backbone that can Enhance Learning Capability of CNN,” Proc. - CVPR 2020 open access, 2020.
  • [20] T. Huang, M. Cheng, Y. Yang, X. Lv, J. Xu. “Tiny Object Detection based on YOLOv5,” Proc. - 5th International Conference on Image and Graphics Processing, 2022.
  • [21] K. Jiang, T. Xie, R. Yan, X. Wen, D. Li, H. Jiang, N. Jiang, L. Feng, X. Duan, and J. Wang. “An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation,”. Proc. - Internet and Computers for Agriculture, 2022.
  • [22] R. Ju, W. Cai, “Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm,” Proc. - arXiv preprint arXiv:2304.05071v5, 2023.
  • [23] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia. “Path Aggregation Network for Instance Segmentation,” Proc. - arXiv preprint arXiv:1612.03144, 2017.
  • [24] T. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie. “Feature Pyramid Networks for Object Detection,” Proc. - arXiv preprint arXiv:1803.01534, 2018.
  • [25] J. Terven, D. Cordova-Esparza, and J. Romero-Gonzalez. “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Proc. - Machine Learning and Knowledge Extraction, 2023.
  • [26] M. Everingham, L. Gool, C. Williams, J. Winn and A. Zisserman. “The PASCAL Visual Object Classes (VOC) Challenge,” Proc. - International Journal of Computer Vision, 2009.
  • [27] J. Davis and M. Goadrich. “The relationship between Precision-Recall and ROC curves,” Proc. - 23rd international conference on Machine learning, 2006.
  • [28] R. Padilla, S. Netto and A. Eduardo. “A Survey on Performance Metrics for Object-Detection Algorithms,” Proc. - 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), 2020.
  • [29] Dataset. “Cigarette Computer Vision Project”, 2022. [Online]. Available: https://universe.roboflow.com/smoke-tamtu/cigarette-6ubdv.

Year 2024, , 253 - 263, 31.08.2024

Yernıyaz Bakhytov , Cemil Öz

https://doi.org/10.35377/saucis...1461268

Abstract

References

  • [1] World Health Organization, “Tobacco” 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/tobacco.
  • [2] E. Arkin, N. Yadikar, X. Xu, A. Aysa and K. Ubul. “A survey: object detection methods from CNN to transformer,” Proc. - Multimedia Tools and Applications, 2023.
  • [3] J. Redmon and A. Farhadi. “YOLO9000: Better, Faster, Stronger” Proc. - arXiv preprint arXiv: 1612.08242, 2016.
  • [4] J. Redmon, S. Divvala, R. Girshick and A. Farhadi. “You Only Look Once: Unified, Real-Time Object Detection,” Proc. - arXiv preprint arXiv: 1506.02640, 2016.
  • [5] J. Redmon and A. Farhadi. “YOLOv3: An Incremental Improvement,” Proc. - arXiv preprint arXiv: 1804.02767, 2018.
  • [6] A. Bochkovskiy, C. Wang and M. Liao. “YOLOv4: Optimal Speed and Accuracy of Object Detection,” Proc. - arXiv preprint arXiv: 2004.10934, 2020.
  • [7] D. Jayakumar and S. Peddakrishna. “Performance Evaluation of YOLOv5-based Custom Object Detection Model for Campus-Specific Scenario,” Proc. - International Journal of Experimental Research and Review, 2024.
  • [8] C. Li, L. Li, H. Jiang, K. Weng, Y. Geng, K. Ke, Q. Li, M. Cheng, W. Nie, Y. Li, B. Zhang, Y. Liang, L. Zhou, X. Xu, X. Chu, X. Wei and Wei, X. “YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications,” Proc. - arXiv preprint arXiv: 2209.02976, 2022.
  • [9] C. Wang, A. Bochkovskiy and M. Liao. “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” Proc. - arXiv preprint arXiv: 2207.02696, 2022.
  • [10] Ultralytics YOLOv8 Docs, “Introducing Ultralytics YOLOv8” 2023. [Online]. Available: https://docs.ultralytics.com/#where-to-start.
  • [11] J. R. Macalisang, N. E. Merencilla, D. Ligayo. “Eye-Smoker: A Machine Vision-Based Nose Inference System of Cigarette Smoking Detection using Convolutional Neural Network,” Proc. - 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS), 2020.
  • [12] A. Khan, S. Khan, B. Hassan, and Z. Zheng. “CNN-Based Smoker Classification and Detection in Smart City Application,”. Sensors 2022.
  • [13] C. Santiago, M. Reyes, L. Tria. “Deep Convolutional Neural Network for Detection of Cigarette Smokers in Public Places: A Low Sample Size Training Data Approach,” Proc. - 2022 International Conference on Decision Aid Sciences and Applications (DASA), 2022.
  • [14] D. Zhang, C. Jiao, S. Wang, “Smoking Image Detection Based on Convolutional Neural Networks,” Proc. - 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018.
  • [15] C. Wang, T. Zheng, F. Sun and H. Lia “A Smoking Detection Algorithm Based on Improved YOLOV5,” Proc. - 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), 2023.
  • [16] Q. Ding, X. Dong, W. Guo, W. Zheng and Y. Pan, “Smoking Detection Algorithm Based On Improved YOLOv5,” Proc. - 2023 IEEE 7th Information Technology and Mechatronics Engineering Conference (ITOEC), 2023.
  • [17] J. Peng, C. Wang, Y. Li, H. Chen, “Substation Personnel Smoking Detection Based On GhostNetV2-YOLOv5,” Proc. - 2023 6th International Symposium on Autonomous Systems (ISAS), 2023.
  • [18] F. Ciaglia, F. S. Zuppichini, P. Guerrie, M. McQuade, and J. Solawetz. “Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark,” Proc. - arXiv preprint arXiv: 2211.13523, 2022.
  • [19] C. Wang, H. Mark Liao, Y. Wu, P. Chen, J. Hsieh,and I. Yeh. “CSPNet: A New Backbone that can Enhance Learning Capability of CNN,” Proc. - CVPR 2020 open access, 2020.
  • [20] T. Huang, M. Cheng, Y. Yang, X. Lv, J. Xu. “Tiny Object Detection based on YOLOv5,” Proc. - 5th International Conference on Image and Graphics Processing, 2022.
  • [21] K. Jiang, T. Xie, R. Yan, X. Wen, D. Li, H. Jiang, N. Jiang, L. Feng, X. Duan, and J. Wang. “An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation,”. Proc. - Internet and Computers for Agriculture, 2022.
  • [22] R. Ju, W. Cai, “Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm,” Proc. - arXiv preprint arXiv:2304.05071v5, 2023.
  • [23] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia. “Path Aggregation Network for Instance Segmentation,” Proc. - arXiv preprint arXiv:1612.03144, 2017.
  • [24] T. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie. “Feature Pyramid Networks for Object Detection,” Proc. - arXiv preprint arXiv:1803.01534, 2018.
  • [25] J. Terven, D. Cordova-Esparza, and J. Romero-Gonzalez. “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Proc. - Machine Learning and Knowledge Extraction, 2023.
  • [26] M. Everingham, L. Gool, C. Williams, J. Winn and A. Zisserman. “The PASCAL Visual Object Classes (VOC) Challenge,” Proc. - International Journal of Computer Vision, 2009.
  • [27] J. Davis and M. Goadrich. “The relationship between Precision-Recall and ROC curves,” Proc. - 23rd international conference on Machine learning, 2006.
  • [28] R. Padilla, S. Netto and A. Eduardo. “A Survey on Performance Metrics for Object-Detection Algorithms,” Proc. - 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), 2020.
  • [29] Dataset. “Cigarette Computer Vision Project”, 2022. [Online]. Available: https://universe.roboflow.com/smoke-tamtu/cigarette-6ubdv.
There are 29 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Yernıyaz Bakhytov 0009-0008-5345-8476

Cemil Öz 0000-0001-9742-6021

Early Pub Date August 26, 2024
Publication Date August 31, 2024
Submission Date March 29, 2024
Acceptance Date July 22, 2024
Published in Issue Year 2024

Cite

IEEE Y. Bakhytov and C. Öz, “Cigarette Detection in Images Based on YOLOv8”, SAUCIS, vol. 7, no. 2, pp. 253–263, 2024, doi: 10.35377/saucis...1461268.

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