适用于快速三维形貌测量的二元结构光编码优化方法

基于计算机视觉的疲劳检测具有低侵入性、低成本 的优点,然而光照变化、面部表情、复杂背景等 仍然对检测率造成很大的影响。以卷积神经网络为代表的深度学习以其较强的特征提取能力 和鲁棒性在模 式识别领域取得了成功的应用。本文提出了一种基于级联卷积神经网络(CNN)结构的疲劳检 测算法。首先训练第 1级网络实现人眼与非人眼的分类,使网络充分学习人眼特征,当输入目标图像时,人眼区 域能快速从第 一级网络特征图中分离出来；然后将人眼图像传送给第2级网络检测眼部特征点位置,计算 眼睛张开度并 以此判断测试者眼睛状态,构造疲劳检测模型；最后根据连续多帧的眼睛状态序列,判断测 试者是否处于疲 劳状态。在检测误差为5%时,眼部4个特征点的平均检测正确率为93.10%,单点检测正确率 最高可达97.14%。 测试结果表明,在本文提出方法下眼睛的清醒和疲劳状态有明显的不同,证明本文提出的方 法有效可行,具有较好的应用前景。

Optimization of binary structured light coding for fast 3D shape measurement

In this paper,a new fatigue detection method based on cascade convolutional neur al network (CNN) structure is proposed.Firstly,for learning the eye features,the first-level network is trained to classify the eye and non-ey e regions.The eye region is extracted by the feature maps of the first-level network.Then,the eye images are sent to the nex t network for the eye feature point detection.The eye opening degree is computed to estimate the eye states o f the subject and construct the model of fatigue detection.Finally,whether fatigue driving or not is judged by t he time series of eye states in multi-consecutive frames.At the detection error of 5%,the average detection acc uracy of eye feature points is 93.10% and the highest detection accuracy of a single point is 97.14%.The experi mental results of our method show that the detected eye states based on the proposed method are obviously dif ferent in awake and fatigue states,which proves the proposed method is effective and has a better application prospect.