Smoking Detection using Deep Learning |
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| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-2 |
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| 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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