Guo Miao, Guan Zheng
Airport Management College, Shanghai Civil Aviation College, Shanghai, China.
Department of Operational Management, Shenzhen Airlines Co., Ltd., Shenzhen, China.
PLoS One. 2025 May 7;20(5):e0322404. doi: 10.1371/journal.pone.0322404. eCollection 2025.
In the current context of rapid development of air traffic, the long-time and high-intensity working environment can easily lead to controllers' fatigue state, which in turn affects flight safety. Different from the traditional Mini-Xception pre-training network oriented to the classification task, the study improves it so that it can effectively process multi-dimensional time-series data of air traffic controllers' facial expressions and emotional changes. On its basis, a dynamic time-series data processing module is introduced and combined with a multi-task learning framework and a technique that combines multi-level feature extraction and emotional state analysis to realize the joint recognition of facial expressions and work states, such as fatigue and stress. The experiment findings denotes that the new model has the highest accuracy of 94.36% in detecting eye fatigue, the highest recall rate of 91.68%, and the maximum area under the curve test value of 93.02%. Compared to similar detection models, its average detection time is shortened by 1.9 seconds, with the highest accuracy of 95% in detecting 180 human eye images and an average fatigue detection of 91%. The innovation of the research is to utilize Mini-Xception network for real-time analysis of dynamic features of facial expressions and correlate them with the actual work performance of the controllers, which proposes a new multi-task learning framework, improves the accuracy and stability of the recognition, and provides a new idea and technical support for intelligent monitoring and control of air traffic management system.
在当前空中交通快速发展的背景下，长时间和高强度的工作环境容易导致管制员出现疲劳状态，进而影响飞行安全。与传统的面向分类任务的Mini-Xception预训练网络不同，该研究对其进行了改进，使其能够有效处理空中交通管制员面部表情和情绪变化的多维时间序列数据。在此基础上，引入了动态时间序列数据处理模块，并结合多任务学习框架以及一种将多级特征提取与情绪状态分析相结合的技术，以实现对面部表情和工作状态（如疲劳和压力）的联合识别。实验结果表明，新模型在检测眼部疲劳方面的最高准确率为94.36%，最高召回率为91.68%，曲线下面积测试值最大为93.02%。与类似检测模型相比，其平均检测时间缩短了1.9秒，在检测180张人眼图像时的最高准确率为95%，平均疲劳检测率为91%。该研究的创新之处在于利用Mini-Xception网络对面部表情的动态特征进行实时分析，并将其与管制员的实际工作表现相关联，提出了一种新的多任务学习框架，提高了识别的准确性和稳定性，为空中交通管理系统的智能监控与控制提供了新的思路和技术支持。
