Anti-microsleep: Drowsiness detection

Hassan, Muhammad Muhazill Daniel (2023) Anti-microsleep: Drowsiness detection. Project Report. Melaka, Malaysia, Universiti Teknikal Malaysia Melaka. (Submitted)

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Abstract

By examining facial expressions and responses, the "Anti-Microsleep" project seeks to create an artificial intelligence (AI) system that can identify driver sleepiness. Anti-Microsleep evaluates the complete face to offer a more thorough assessment, whereas existing drowsiness detection systems generally rely on eye-related data. The device features a cloud computing module for data storage and uses image processing algorithms to distinguish faces and eyes. The project's goals include creating an AI system that can detect sleepiness effectively, assessing how well Haar Cascade operate for facial classifier, and improving road safety by lowering the number of accidents brought on by driver weariness. The crucial issue of sleepy driving and its role in accidents is addressed by the Anti-Microsleep initiative. The project seeks to produce a dependable system that can precisely identify and prevent driver sleepiness by utilising AI and facial recognition technology. The initiative has the potential to greatly increase traffic safety, save lives, and progress AI and computer vision technology.

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