Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention Mechanism
The abnormal behavior of cockpit pilots during the manipulation process is an important incentive for flight safety, but the complex cockpit environment limits the detection accuracy, with problems such as false detection, missed detection, and insufficient feature extraction capability. This articl...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/16/2538 |
| _version_ | 1797432231024656384 |
|---|---|
| author | Nongtian Chen Yongzheng Man Youchao Sun |
| author_facet | Nongtian Chen Yongzheng Man Youchao Sun |
| author_sort | Nongtian Chen |
| collection | DOAJ |
| description | The abnormal behavior of cockpit pilots during the manipulation process is an important incentive for flight safety, but the complex cockpit environment limits the detection accuracy, with problems such as false detection, missed detection, and insufficient feature extraction capability. This article proposes a method of abnormal pilot driving behavior detection based on the improved YOLOv4 deep learning algorithm and by integrating an attention mechanism. Firstly, the semantic image features are extracted by running the deep neural network structure to complete the image and video recognition of pilot driving behavior. Secondly, the CBAM attention mechanism is introduced into the neural network to solve the problem of gradient disappearance during training. The CBAM mechanism includes both channel and spatial attention processes, meaning the feature extraction capability of the network can be improved. Finally, the features are extracted through the convolutional neural network to monitor the abnormal driving behavior of pilots and for example verification. The conclusion shows that the deep learning algorithm based on the improved YOLOv4 method is practical and feasible for the monitoring of the abnormal driving behavior of pilots during the flight maneuvering phase. The experimental results show that the improved YOLOv4 recognition rate is significantly higher than the unimproved algorithm, and the calling phase has a mAP of 87.35%, an accuracy of 75.76%, and a recall of 87.36%. The smoking phase has a mAP of 87.35%, an accuracy of 85.54%, and a recall of 85.54%. The conclusion shows that the deep learning algorithm based on the improved YOLOv4 method is practical and feasible for the monitoring of the abnormal driving behavior of pilots in the flight maneuvering phase. This method can quickly and accurately identify the abnormal behavior of pilots, providing an important theoretical reference for abnormal behavior detection and risk management. |
| first_indexed | 2024-03-09T09:58:20Z |
| format | Article |
| id | doaj.art-70b30f530c96428980e5525354bf95be |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T09:58:20Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-70b30f530c96428980e5525354bf95be2023-12-01T23:38:23ZengMDPI AGElectronics2079-92922022-08-011116253810.3390/electronics11162538Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention MechanismNongtian Chen0Yongzheng Man1Youchao Sun2College of Aviation Engineering, Civil Aviation Flight University of China, Guanghan 618307, ChinaCollege of Civil Aviation Safety Engineering, Civil Aviation Flight University of China, Guanghan 618307, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaThe abnormal behavior of cockpit pilots during the manipulation process is an important incentive for flight safety, but the complex cockpit environment limits the detection accuracy, with problems such as false detection, missed detection, and insufficient feature extraction capability. This article proposes a method of abnormal pilot driving behavior detection based on the improved YOLOv4 deep learning algorithm and by integrating an attention mechanism. Firstly, the semantic image features are extracted by running the deep neural network structure to complete the image and video recognition of pilot driving behavior. Secondly, the CBAM attention mechanism is introduced into the neural network to solve the problem of gradient disappearance during training. The CBAM mechanism includes both channel and spatial attention processes, meaning the feature extraction capability of the network can be improved. Finally, the features are extracted through the convolutional neural network to monitor the abnormal driving behavior of pilots and for example verification. The conclusion shows that the deep learning algorithm based on the improved YOLOv4 method is practical and feasible for the monitoring of the abnormal driving behavior of pilots during the flight maneuvering phase. The experimental results show that the improved YOLOv4 recognition rate is significantly higher than the unimproved algorithm, and the calling phase has a mAP of 87.35%, an accuracy of 75.76%, and a recall of 87.36%. The smoking phase has a mAP of 87.35%, an accuracy of 85.54%, and a recall of 85.54%. The conclusion shows that the deep learning algorithm based on the improved YOLOv4 method is practical and feasible for the monitoring of the abnormal driving behavior of pilots in the flight maneuvering phase. This method can quickly and accurately identify the abnormal behavior of pilots, providing an important theoretical reference for abnormal behavior detection and risk management.https://www.mdpi.com/2079-9292/11/16/2538pilot abnormal behaviorbehavior detectionYOLOv4 algorithmCBAMflight safety |
| spellingShingle | Nongtian Chen Yongzheng Man Youchao Sun Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention Mechanism Electronics pilot abnormal behavior behavior detection YOLOv4 algorithm CBAM flight safety |
| title | Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention Mechanism |
| title_full | Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention Mechanism |
| title_fullStr | Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention Mechanism |
| title_full_unstemmed | Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention Mechanism |
| title_short | Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention Mechanism |
| title_sort | abnormal cockpit pilot driving behavior detection using yolov4 fused attention mechanism |
| topic | pilot abnormal behavior behavior detection YOLOv4 algorithm CBAM flight safety |
| url | https://www.mdpi.com/2079-9292/11/16/2538 |
| work_keys_str_mv | AT nongtianchen abnormalcockpitpilotdrivingbehaviordetectionusingyolov4fusedattentionmechanism AT yongzhengman abnormalcockpitpilotdrivingbehaviordetectionusingyolov4fusedattentionmechanism AT youchaosun abnormalcockpitpilotdrivingbehaviordetectionusingyolov4fusedattentionmechanism |