A Novel Aircraft Refueling Behavior Detection Model based on Deep Learning. Issue 1 (January 2021)
- Record Type:
- Journal Article
- Title:
- A Novel Aircraft Refueling Behavior Detection Model based on Deep Learning. Issue 1 (January 2021)
- Main Title:
- A Novel Aircraft Refueling Behavior Detection Model based on Deep Learning
- Authors:
- He, Ran
Wang, Jianxing
Zhang, Zhengning - Abstract:
- Abstract: How to efficiently and accurately monitor the sparse aircraft Refueling behaviors from a large amount of video streams is of great significant for improving the level of management and refueling efficiency of aviation fuel stations. Due to the COVID-19 virus epidemic, the number of flights has dropped severely, the collection of image samples for refueling behaviors from large airport becomes difficult, which hinders the real-time detection of Refueling behaviors and reduces the efficiency of aviation fuel station. Therefore, automatically detecting the refueling behaviors of each station in time and accurately from a large number of aviation refuel stations still keeps challenging. To address this challenge, we propose a novel aircraft refueling behavior detection model based on deep learning, to quickly and accurately determine the refueling behaviors through analysing the video stream collected from the massive cameras deployed in the airport. Our proposed model adopts Inception v3 architecture of ImageNet to realize the model capability of transfer learning, the data augmentation to address the issue of overfitting, and the mAP (mean Average Precision) to test the performance. Our proposed model is also applied in the detection of refueling behaviors in China National Aviation Fuel Group, LTD (CNAF). The practical application results show better performance than other existing methods. Our work will promote the updating of related industry standard.
- Is Part Of:
- Journal of physics. Volume 1732:Issue 1(2021)
- Journal:
- Journal of physics
- Issue:
- Volume 1732:Issue 1(2021)
- Issue Display:
- Volume 1732, Issue 1 (2021)
- Year:
- 2021
- Volume:
- 1732
- Issue:
- 1
- Issue Sort Value:
- 2021-1732-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-01
- Subjects:
- Physics -- Congresses
530.5 - Journal URLs:
- http://www.iop.org/EJ/journal/1742-6596 ↗
http://ioppublishing.org/ ↗ - DOI:
- 10.1088/1742-6596/1732/1/012058 ↗
- Languages:
- English
- ISSNs:
- 1742-6588
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5036.223000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25481.xml