Driver’s Drowsiness Detection using MobileNet on Web

Drowsiness detection is an important aspect of ensuring safety on the road, at work, and in various other activities where alertness is critical. With the increasing use of mobile devices and the internet, there is a growing need for a drowsiness detector that can be used on web-based platforms. This research paper presents a novel drowsiness detection system that uses MobileNet, a deep convolutional neural network, for real-time detection of drowsiness. The proposed system utilizes facial feature extraction and classification to detect drowsiness and provides an alert in real-time. The experimental results show the effectiveness of the proposed system in detecting drowsiness with high accuracy and low latency. This system can be used as a tool to promote safety in various activities and has potential applications in areas such as transportation, healthcare, and workplace safety.