论文标题
SleepyWheels：嗜睡检测的合奏模型，导致预防事故
SleepyWheels: An Ensemble Model for Drowsiness Detection leading to Accident Prevention
论文作者
论文摘要
由于驾驶员在方向盘后入睡，大约有40％的与印度高速公路驾驶有关的事故发生。正在进行几种类型的研究来检测驾驶员的嗜睡，但它们遭受了模型的复杂性和成本。在本文中，SleepyWheels提出了一种革命性的方法，该方法将使用轻量级神经网络与面部标志性识别结合使用，以实时识别驾驶员疲劳。 SleepyWheels在广泛的测试场景中取得了成功，包括缺乏面部特征，同时遮盖了眼睛或口腔，驾驶员会改变肤色，相机位置和观察角。模拟实时系统时，它可以很好地工作。 SleepyWheels利用了有效的NETV2和面部标志性检测器来识别嗜睡检测。该模型在专门创建的有关驾驶员嗜睡的数据集上进行了培训，其准确度为97％。该模型是轻巧的，因此可以作为各种平台的移动应用程序进一步部署。
Around 40 percent of accidents related to driving on highways in India occur due to the driver falling asleep behind the steering wheel. Several types of research are ongoing to detect driver drowsiness but they suffer from the complexity and cost of the models. In this paper, SleepyWheels a revolutionary method that uses a lightweight neural network in conjunction with facial landmark identification is proposed to identify driver fatigue in real time. SleepyWheels is successful in a wide range of test scenarios, including the lack of facial characteristics while covering the eye or mouth, the drivers varying skin tones, camera placements, and observational angles. It can work well when emulated to real time systems. SleepyWheels utilized EfficientNetV2 and a facial landmark detector for identifying drowsiness detection. The model is trained on a specially created dataset on driver sleepiness and it achieves an accuracy of 97 percent. The model is lightweight hence it can be further deployed as a mobile application for various platforms.
