Smoking Detection using Deep Learning

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© 2023 by IJCTT Journal
Volume-71 Issue-2
Year of Publication : 2023
Authors : Madabhushi Aditya, Pranav Reddy Gudpati, K Sai Sidhartha Reddy, Priyanshu, Radha Karampudi
DOI :  10.14445/22312803/IJCTT-V71I2P102

How to Cite?

Madabhushi Aditya, Pranav Reddy Gudpati, K Sai Sidhartha Reddy, Priyanshu, Radha Karampudi, "Smoking Detection using Deep Learning," International Journal of Computer Trends and Technology, vol. 71, no. 2, pp. 8-14, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I2P102

Abstract
This paper presents a novel approach for identifying smoking behavior using deep learning to extract important features from an image. The approach involves using deep learning to identify key regions in an image and a conditional detection system built using YOLOv5 to improve performance and simplify the model. The method was tested on a dataset containing 7,000 images with equal representation of smokers and non-smokers in various settings. The effectiveness of the technique was evaluated using both quantitative and qualitative measures, resulting in a classification accuracy of 96.74% on the dataset.

Keywords
Deep Learning, YOLOv5, Quantitative Measures, Qualitative Measures.

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