A Machine Intelligence Model to Detect Drowsiness for Preventing Road Accidents

Doppala, Bhanu Prakash; Vamsi, Bandi; Bhattacharyya, Debnath; Rao, J.Nageswara

doi:10.1109/icaaic53929.2022.9793256

Cited by 1 publication

(1 citation statement)

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“…Several recent studies have adopted a neural network approach to detect short periods of sleepiness. Doppala et al [22] proposed a drowsiness identification system by capturing image frames of the driver's face from the video stream. In this study, a deep convolutional neural network (D-CNN) model is suggested to detect drowsiness under various circumstances, such as drivers with and without glasses.…”

Section: Introductionmentioning

confidence: 99%

Effective driver distraction warning system incorporating fast image recognition methods

Nguyen,

Trinh

2024

IJECE

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Modern cars are equipped with advanced automatic technology featuring various safety measures for car occupants. However, the growing density of vehicles, especially in areas where infrastructure development lags, poses potential dangers, particularly accidents caused by driver subjectivity. These incidents may occur due to driver distraction or the presence of high-risk obstacles on the road. This article presents a comprehensive solution to assist drivers in mitigating these risks. Firstly, the study introduces a novel method to enhance the recognition of a driver's facial features by analyzing benchmarks and the whites of the eyes to assess the distraction level. Secondly, a domain division method is proposed to identify obstacles and lanes in front of the vehicle, enabling the assessment of the danger level. This information is promptly relayed to the driver and relevant individuals, such as the driver's manager or supervisor. An experimental device has also been developed to evaluate the effectiveness of the algorithms, solutions, and processing capabilities of the system.

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Section: Introductionmentioning

confidence: 99%