基于DCNN的管制员疲劳状态检测

空中交通管制员疲劳工作势必会严重威胁空中交通安全,通过对眼睛睁闭状态判定是现阶段对管制员疲劳检测的一种主要方式。为检测管制员疲劳状态,提出了一种基于迁移学习的DCNN眼睛状态识别模型。首先,利用深度级联神经网络的MTCNN算法检测出管制员面部区域,并实现对面部5个关键点标定和眼睛的定位;然后将检测到的眼睛图像传入到预训练的DCNN眼睛状态分类模型,识别眼睛的睁闭眼状态;最后结合PERCLOSE 80指标检测管制员疲劳状态。分别在ZJU、CEW和ATCE数据集上,对DCNN、VGG16、InceptionV3、ResNet50四种模型的准确率、损失率和F1分数指标进行对比实验。实验结果表明：在ZJU和CEW数据集上,DCNN眼睛状态分类模型检测准确率为97%,较VGG16、InceptionV3、ResNet50等模型进行眼部状态分类任务,DCNN模型的F1分数有3%至7%的提高。在ATCE数据集上DCNN模型检测准确率达到98.35%,F1分数达到98.06%,验证了DCNN模型的有效性与准确性。

Controller Fatigue State Detection Based on DCNN

The fatigue work of air traffic controllers is bound to seriously threaten the air traffic safety. It is a main way to detect the fatigue of air traffic controllers by judging the state of eyes open and closed. In order to detect the controller''s fatigue state, a DCNN eye state recognition model based on transfer learning is proposed. Firstly, the MTCNN algorithm of deep cascaded neural network is used to detect the controller''s face area. The five key points of the face are calibrated and the eyes are located. Secondly, the detected eye images are transferred to the pre trained DCNN eye state classification model for recognizing the open and closed eyes. Finally, the controller''s fatigue state is detected combined with PERCLOSE 80 index. The accuracy, loss rate and F1 score of DCNN, VGG16, InceptionV3 and ResNet50 models were compared on ZJU, CEW and ATCE datasets. Experimental results show that on ZJU and CEW datasets, the detection accuracy of DCNN eye state classification model is 97%, compared with VGG16, InceptionV3, ResNet50 models for eye state classification tasks, DCNN model F1 score has been improved by 3% to 7%. The accuracy of DCNN model detection reached 98.35 percent and the accuracy of F1 score reached 98.06 percent, which verified the validity and accuracy of DCNN model.