| 基于卷积神经网络面部图像识别的拖拉机驾驶员疲劳检测 |
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| 引用本文： | 卢伟,胡海阳,王家鹏,王玲,Yiming Deng.基于卷积神经网络面部图像识别的拖拉机驾驶员疲劳检测[J].农业工程学报,2018,34(7):192-199. |
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| 作者姓名： | 卢伟 胡海阳 王家鹏 王玲 Yiming Deng |
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| 作者单位： | 1. 南京农业大学工学院，南京 210031；2. 江苏省现代设施农业技术与装备工程实验室，南京 210031；,1. 南京农业大学工学院，南京 210031；,1. 南京农业大学工学院，南京 210031；,1. 南京农业大学工学院，南京 210031；,3. Michigan State University，College of Engineering，East Lansing，48824 |
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| 基金项目： | 国家自然科学基金青年基金（51405239）；江苏省农机三新工程项目（SZ120170036）；Asia hub on WEF and Agriculture，NAU-MSU联合研究项目（2017-H-11） |
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| 摘 要： | 针对疲劳驾驶极易造成拖拉机交通事故这一问题,该文提出了一种基于卷积神经网络面部特征识别的拖拉机驾驶员疲劳检测方法。首先,利用伽马亮度校正对驾驶员面部图像进行光照预处理,再通过小波包去除图像中的椒盐噪声和高斯噪声,对预处理后的图像分别通过PCA-SCM人脸特征识别定位算法和基于人脸核心特征库及肤色模型的人脸识别算法进行驾驶员面部的识别定位,并通过比对这2种算法识别的偏差大小校验算法识别的有效性,以减小拖拉机工作振动时采样对图像中人脸定位精度的影响。将提取到的驾驶员面部图像输入到卷积神经网络进行深度学习和训练,并建立驾驶员疲劳视觉检测模型,从而实现基于拖拉机驾驶员面部图像的疲劳检测。统计训练过程中各项参数变化情况并进行T-SNE降维迭代分析,与其他常规方法相比,CNN在检测准确度和检测效率方面都有较为明显的优势。试验表明,所提出的检测模型准确率98.9%,图片识别效率38 ms/帧(Inter i7-4510U双核处理器),能够实现拖拉机驾驶员疲劳状况的实时检测,该研究可为解决疲劳驾驶这一安全问题提供参考。
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| 关 键 词： | 拖拉机 图像处理 算法 疲劳驾驶 PCA-SCM 卷积神经网络 面部识别 |
| 收稿时间： | 2017/8/18 0:00:00 |
| 修稿时间： | 2018/2/1 0:00:00 |
Tractor driver fatigue detection based on convolution neural network and facial image recognition |
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| Lu Wei,Hu Haiyang,Wang Jiapeng,Wang Ling and Yiming Deng.Tractor driver fatigue detection based on convolution neural network and facial image recognition[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(7):192-199. |
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| Authors: | Lu Wei Hu Haiyang Wang Jiapeng Wang Ling and Yiming Deng |
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| Institution: | 1. Department of Engineering, Nanjing Agricultural University, Nanjing 210031, China; 2. Jiangsu Province Engineering Laboratory of Modern Facility Agriculture Technology and Equipment, Nanjing 210031, China;,1. Department of Engineering, Nanjing Agricultural University, Nanjing 210031, China;,1. Department of Engineering, Nanjing Agricultural University, Nanjing 210031, China;,1. Department of Engineering, Nanjing Agricultural University, Nanjing 210031, China; and 3. Michigan State University, College of Engineering, East Lansing, 48824, America |
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| Abstract: | Abstract: Tractor is a popular tool for agricultural farming in the world. But many factors such as high labor intensity, absence of sleeping and monotonous environment make driver fatigued easily in farming season. Aiming at the phenomenon of fatigue driving which is the major reason for tractor traffic accidents, a tractor fatigue detection method based on facial feature recognition using convolution neural network was proposed. Firstly, 1200 driving images were sampled during different daytimes by 60 drivers in which the male accounted for 77%. Then the face images of tractor drivers were pretreated by gamma intensity correction (GIC) method, aimed to solve the problem of the uneven brightness of images with fast processing speed because of its simple algorithm, and hence reduce the influence of illumination of faces. Due to the complex working environment of the tractor and the special surrounding window design, wavelet denoising method was applied in image denoising because it is powerful for the removal of impulse noise and Gaussian noise which are the main noise in the images. In addition, principal component analysis - skin color model (PCA-SCM) method was used to detect and locate faces and then skin color model was applied to rectify and extract the facial area. For improving the face recognition precision, dual-face recognition and checking algorithm was proposed. Firstly, core feature face database was established and human face recognition classifier was generated. Secondly the core feature memory space of human face was formed to locate human face in image. Thirdly, to establish a Gaussian skin color model in YCbCr color space and perform the binary converting, bloat and corrosion methods were also applied to remove fake areas for improving accuracy. In the end, the obtained face position was compared with the previous one to evaluate the effectiveness of this image. Finally, the drivers'' facial images were input to the convolution neural network (CNN) for training, and the driver fatigue detection model was established to identify the fatigue of tractor driver based on the face image. The neural network was mainly composed of an input layer, 2 convolution layers, 2 pooling layers, 2 fully connected layers and an output layer. The batch size was 30, and the convolution layer sampled 16 channels of input layer. After reducing the dimension of the feature map by the pooling layer, the full connection layer classified the extracted features and then the correctness rate and the loss rate were obtained. The AdamOptimizer was selected as the optimizer to improve the gradient descent by using the momentum (moving average of the parameters). The hyperparameters were dynamically adjusted to minimize the loss function of the network to achieve better discrimination. Additionally, the changes of weight parameter and bias parameter in convolutional layer and all-connected layer were analyzed, as well as the change trend of correctness rate and loss rate. The T-SNE dimensionality reduction iterative analysis was applied in CNN training. Compared with other methods of fatigue driving detection, such as dynamic template matching and BP (back propagation) neural network, CNN has obvious advantages in detecting correctness and detecting speed. Due to the unique convolutional kernel structure of CNN, more and more useful information in the image can be extracted more efficiently and quickly. CNN still has good detection efficiency compared with the fuzzy inference detection method. Experiments showed that the proposed detection model''s accuracy rate is 98.9%, and the recognition time for each frame of image is 38 ms (Using Inter i7-4510U dual-core processor), which demonstrate that the proposed image processing method using CNN can realize fatigue detection of tractor driver in real time. |
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| Keywords: | tractors image processing alogrithms fatigue driving PCA-SCM convolution neural network face recognition |
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