Abstract
A humongous number of road accidents occur every year and a substantial amount of these cases are due to the drowsy condition of the driver. A driver's well-being on the road decides the fate of fellow passengers. A drowsy driver is indeed a great threat to many lives. A large population is directly or indirectly affected by these situations. In case of any mishap, these vehicles cause huge damage to both life and property. Driving drowsy is as dangerous as driving drunk. In both scenarios, the driver has no control over the vehicle. The best way to avoid such accidents caused by a driver's drowsy condition is to detect his/her drowsiness and warn him/her before he/she falls asleep. Being drowsy and skipping traffic signs due to drowsiness are a point of major concern for road accidents. To combat such hazardous situations, we have come up with an innovative idea in which we would get the complete analysis of the driver's condition and sleep pattern and alert him with a beep sound. This innovative yet useful project is called the drowsiness detection system. Firstly, the system preprocesses the image to focus on important information. Secondly, detection, binarization, and localization are implemented. Finally, classifications are made of the traffic signs which are detected based on deep learning. This document proposes a method of detection of drowsiness and recognition of traffic signs based on image processing, combined with the convolution neural network (CNN). Being able to accurately and effectively identify road signs can improve driving safety. Traffic signs provide general and useful information on traffic rules, road conditions, and driving directions to road users whether they are passengers, drivers, or pedestrians. Neglection or skipping of traffic signs by drivers due to any reason is a threat to all road users and our project provides a solution to help drivers drive better and safer.
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Pandey, R., Bhasin, P., Popli, S., Sharma, M., Sharma, N. (2023). Driver Drowsiness Detection and Traffic Sign Recognition System. In: Dutta, P., Chakrabarti, S., Bhattacharya, A., Dutta, S., Piuri, V. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4193-1_3
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DOI: https://doi.org/10.1007/978-981-19-4193-1_3
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