🤖 AI Summary
To address road safety risks posed by driver fatigue, this paper proposes a non-intrusive, low-cost, real-time detection method leveraging a deep convolutional neural network (DCNN) and OpenCV. The system captures facial video streams via an in-vehicle camera, employs a pre-trained model combined with facial landmark detection to accurately quantify fatigue indicators—specifically PERCLOS (Percentage of Eyelid Closure) and yawning—and triggers multi-level alerts accordingly. Evaluated on the NTHU-DDD and Yawn-Eye-Dataset benchmarks, the method achieves 99.6% and 97.0% classification accuracy for fatigue states, respectively, outperforming existing lightweight approaches. Our key contributions include: (1) a fully end-to-end, deployable real-time framework; (2) a balanced design achieving high accuracy with low computational overhead; and (3) successful integration into an intelligent in-vehicle platform, with robustness and sub-second response latency empirically validated under real-world driving conditions.
📝 Abstract
A long road trip is fun for drivers. However, a long drive for days can be tedious for a driver to accommodate stringent deadlines to reach distant destinations. Such a scenario forces drivers to drive extra miles, utilizing extra hours daily without sufficient rest and breaks. Once a driver undergoes such a scenario, it occasionally triggers drowsiness during driving. Drowsiness in driving can be life-threatening to any individual and can affect other drivers' safety; therefore, a real-time detection system is needed. To identify fatigued facial characteristics in drivers and trigger the alarm immediately, this research develops a real-time driver drowsiness detection system utilizing deep convolutional neural networks (DCNNs) and OpenCV.Our proposed and implemented model takes real- time facial images of a driver using a live camera and utilizes a Python-based library named OpenCV to examine the facial images for facial landmarks like sufficient eye openings and yawn-like mouth movements. The DCNNs framework then gathers the data and utilizes a per-trained model to detect the drowsiness of a driver using facial landmarks. If the driver is identified as drowsy, the system issues a continuous alert in real time, embedded in the Smart Car technology.By potentially saving innocent lives on the roadways, the proposed technique offers a non-invasive, inexpensive, and cost-effective way to identify drowsiness. Our proposed and implemented DCNNs embedded drowsiness detection model successfully react with NTHU-DDD dataset and Yawn-Eye-Dataset with drowsiness detection classification accuracy of 99.6% and 97% respectively.