Abstract
Traffic accidents pose an increasing threat to society, and researchers are dedicated to preventing accidents and reducing fatalities, as highlighted by the World Health Organization. One significant cause of accidents is drowsy driving, which often leads to severe injuries and loss of life. The objective of this research is to create a fatigue detection system that can effectively minimize accidents associated with exhaustion. The system utilizes facial recognition technology to identify drowsy drivers by analyzing eye patterns through video processing. When the level of fatigue surpasses a predetermined threshold, the system alerts the driver and adjusts the vehicle's acceleration accordingly. The implementation of OpenCv libraries, such as Haar-cascade, along with Raspberry Pi facilitates seamless integration of the system. This dissertation evaluates advancements in computational engineering for the development of a fatigue detection system to mitigate accidents caused by drowsiness. It offers valuable insights and recommendations to enhance comprehension and optimize the system's effectiveness, ultimately leading to safer road travel.
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