IOT based Driver Drowsiness and Alcohol/Smoke Detection System Using Deep Learning Algorithm

Author(s)	Prof. Ms. Trusha Wagh, Rohit Bangar, Ketan Botre, Krishna Hingane
Country	India
Abstract	Due to extended driving times and exhaustion, driver drowsiness is one of the main causes of traffic accidents, especially for drivers of large vehicles (like buses and heavy trucks). Driver drowsiness can also be found due to consumption of alcohol Under operational circumstances. In this study, we offer an easy-to-deploy and flexible vision-based tiredness detection system for bus driver monitoring in big vehicles, including detection, face detection, eye detection, eye openness estimation, and alcohol. The following are the primary novel techniques: 1) A spectral regression-based method to estimate the continuous degree of eye openness; 2) A fusion algorithm that uses adaptive integration on the multi-model detections of both eyes to estimate the eye state. 3) An alcohol detection sensor that will detect the presence of alcohol molecules. When a camera with an oblique viewing angle to the driver's face is employed for driving status monitoring, the experimental results demonstrate the benefits of the system in terms of accuracy and robustness for difficult conditions
Keywords	Raspberry Pi, Camera, Machine learning, Video Analysis, MQ 3 sensor.
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-29

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International Journal For Multidisciplinary Research