论文标题
将您的AI-ES留在道路上：通过卷积神经网络和针对性的数据增加来解决分心的驾驶员检测
Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targeted Data Augmentation
论文作者
论文摘要
根据世界卫生组织的说法，分心的驾驶是世界上发生事故和死亡的主要原因之一。在我们的研究中，我们通过建立一个强大的多级分类器来解决分心驾驶问题的问题，以使用州农场分散注意力的驾驶数据集来检测和识别不同形式的驾驶员。我们利用经过审慎的图像分类模型的组合，经典数据增强，基于OPENCV的图像预处理和皮肤分割增强方法的组合。我们最佳性能模型结合了几种增强技术，包括皮肤分割，面部模糊和经典的增强技术。该模型比基线的F1得分提高了约15％，因此显示了这些技术在增强神经网络的功能方面对分心驱动器检测任务的承诺。
According to the World Health Organization, distracted driving is one of the leading cause of motor accidents and deaths in the world. In our study, we tackle the problem of distracted driving by aiming to build a robust multi-class classifier to detect and identify different forms of driver inattention using the State Farm Distracted Driving Dataset. We utilize combinations of pretrained image classification models, classical data augmentation, OpenCV based image preprocessing and skin segmentation augmentation approaches. Our best performing model combines several augmentation techniques, including skin segmentation, facial blurring, and classical augmentation techniques. This model achieves an approximately 15% increase in F1 score over the baseline, thus showing the promise in these techniques in enhancing the power of neural networks for the task of distracted driver detection.
