Smoking Driving Behavior Detection Based on Deep Learning

Authors

  • Jinfan Huang
  • Rong Li

DOI:

https://doi.org/10.54097/ajst.v5i2.6049

Keywords:

Deep Learning, Computer Vision, Convolutional Neural Network, Smoking Driving.

Abstract

Smoking in driving not only reduces the accuracy of driving operation, but also leads to insufficient oxygen and higher possibility of traffic accident. Therefore, considering the safety of life and avoiding accidents as far as possible, a detection model based on deep learning which can quickly detect smoking driving behavior is designed. In this model, convolutional neural network is used to process the input frames of the video stream captured by the camera. After the shape feature extraction, fuzzy feature processing, motion feature detection and color feature region comparison, the smoking driving behavior can be judged. Through the design of a series of computer vision detection modules, not only can reduce the calculation of the model, but also improve the efficiency of deduction, so as to meet the performance requirements of real-time monitoring. In order to quickly find out the driver smoking behavior and trigger warning, so as to avoid unnecessary traffic accidents and ensure life safety.

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References

LinYL Dollar P, Girshick RB, et al. Feature pyramid networks for object detection[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, July 21-26, 2017. Washington: IEEE Computer Society, 2017,1(2): 4.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.

Woo S, Park JC, Lee JY, et al. CBAM: Convolutional block attention module[C]//LNCS 11211: Proceedings of the 15th European Conference on Computer Vision, Munich, September 8-14, 2018. Heidelberg: Springer, 2018, 3-19.B. Smith, “An approach to graphs of linear forms (Unpublished work style),” unpublished.

Yun S, Han D, Chun S, et al. CutMix: Regularization strategy to train strong classifiers with localizable features.[C]// Proceedings of the IEEE International Conference on Computer Vision, Seoul, October 27- November 2, 2019. Washington: IEEE Computer Society, 2019, 6023–6032.

Zheng ZH, Wang P, Liu W, et al. Distance-IoU Loss: Faster and better learning for bounding box regression.[J] Proceedings of the 34th AAAI Conference on Artifificial Intelligence, 2020, 34(7): 12993-13000.

Senyurek VY , Imtiaz MH , Belsare P , et al. Smoking detection based on regularity analysis of hand to mouth gestures[J]. Biomedical Signal Processing and Control, 2019, 51: 106-112.

Senyurek V, Imtiaz M, et al. Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter[J]. Sensors, 2019, 19(3): 570-588.

Ye S, Bai Z, Chen H, et al. An effective algorithm to detect both smoke and flame using color and wavelet analysis[J]. Pattern Recognition and Image Analysis, 2017, 27: 131-138.

Yan B, Zhang JL. Research on Image Translation Based on Generative Adversarial Network[J]. Foreign Electronic Measurement Technology, 2019,38 (6)130-134.

Sun X, Li XG, Li JF, et al. Research Progress of image super resolution Restoration Based on Deep Learning [J]. Acta Automatica Sinica, 2017,43(5):697-709.

Du QG, Zhai XC, Wen Q, et al. Recursive Error Analysis of Complex Assembly Based on Rigid Body Kinematics[J]. Journal of South China University of Technology (Natural Science Edition), 2017,45 (9):26-33.

Zhao M, Zhang W, Wang X, et al. Smoke detection based on spatio-temporal background model combined with various texture features[J]. Journal of Xi 'an Jiaotong University, 2018, 52(8):72-78.

Muhammad K, Ahmad J, Mehmood I, et al. Convolutional neural networks based fire detection in surveillance videos[J]. Ieee Access, 2018, 6: 18174-18183.

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Published

19-03-2023

Issue

Section

Articles

How to Cite

Smoking Driving Behavior Detection Based on Deep Learning. (2023). Academic Journal of Science and Technology, 5(2), 59-62. https://doi.org/10.54097/ajst.v5i2.6049

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