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Air traffic controller work state recognition based on improved xception network.

Miao Guo¹, Zheng Guan²

¹Airport Management College, Shanghai Civil Aviation College, Shanghai, China.

|May 7, 2025

Summary

This summary is machine-generated.

This study enhances the Mini-Xception network to detect air traffic controller fatigue from facial expressions, improving flight safety. The new model achieves high accuracy in identifying eye fatigue and work states in real-time.

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Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

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Published on: April 4, 2025

Area of Science:

Human-Computer Interaction
Artificial Intelligence
Aerospace Engineering

Background:

Air traffic control involves long, intense work hours, increasing controller fatigue and risking flight safety.
Existing pre-training networks are often limited to classification tasks, not dynamic emotional state analysis.
Real-time monitoring of controller well-being is crucial for maintaining high safety standards.

Purpose of the Study:

To develop an improved Mini-Xception network capable of processing multi-dimensional time-series facial expression and emotional data.
To introduce a dynamic time-series processing module and a multi-task learning framework for joint recognition of facial expressions and work states (fatigue, stress).
To enhance the accuracy, stability, and real-time analysis of controller fatigue detection.

Main Methods:

Modified the Mini-Xception network to process dynamic, multi-dimensional time-series data of facial expressions.
Integrated a dynamic time-series data processing module with a multi-task learning framework.
Employed multi-level feature extraction and emotional state analysis for joint recognition of expressions and work states.

Main Results:

Achieved 94.36% accuracy and 91.68% recall in detecting eye fatigue.
Demonstrated a maximum area under the curve (AUC) of 93.02% for fatigue detection.
Reduced average detection time by 1.9 seconds compared to similar models, with 91% average fatigue detection accuracy on human eye images.

Conclusions:

The enhanced Mini-Xception network effectively analyzes dynamic facial features for real-time monitoring of air traffic controller fatigue and stress.
The novel multi-task learning framework improves recognition accuracy and stability, offering technical support for intelligent air traffic management.
This research provides a new approach for intelligent monitoring systems in air traffic management, prioritizing controller well-being and flight safety.