Aviation Rivet Classification and Anomaly Detection Based on Deep Learning
The shortage of personnel and the high cost have become a major pain point in the current safety supervision work of the inspectors. Aiming at the problem that the aircraft maintenance inspector could not visit the scene in person during the epidemic, a remote safety supervision platform was built b...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/3546838 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850174894176731136 |
|---|---|
| author | Xiao-bo Zhu |
| author_facet | Xiao-bo Zhu |
| author_sort | Xiao-bo Zhu |
| collection | DOAJ |
| description | The shortage of personnel and the high cost have become a major pain point in the current safety supervision work of the inspectors. Aiming at the problem that the aircraft maintenance inspector could not visit the scene in person during the epidemic, a remote safety supervision platform was built based on intelligent glasses and 5G network, and the real-time monitoring of the aircraft skin rivet status was realized. And a method of aviation rivet classification and anomaly detection based on deep learning algorithm was proposed. Firstly, according to the appearance of rivet head, the aviation rivet is classified, the data set of aviation rivet is made, and the aviation rivet classification and anomaly detection model are constructed. Evaluate the detection results from such indicators as confidence, precision, recall rate, and mAP and compare the algorithm with the detection results of Yolox-s, Yolox-m, Yolov5-s, Yolov5-m, and Yolov4. The results show that (1) the algorithm proposed in this paper can realize the classification of aviation rivets and the detection of abnormal conditions, the confidence of the detection results is more than 90%, and the average precision, recall, and AP value are above 95%, 85%, and 88%, respectively. (2) The order of rivet classification and abnormal detection effect from good to bad is Philips screws, round head rivets, flat head rivets, countersunk head rivets, blind rivets, and abnormal condition. (3) Compared with other algorithms, the aviation rivet classification abnormal target detection based on deep learning has absolute advantages in accuracy and speed. |
| format | Article |
| id | doaj-art-d6e244b15fc44c95946f72aed08991ee |
| institution | OA Journals |
| issn | 1687-5974 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-d6e244b15fc44c95946f72aed08991ee2025-08-20T02:19:34ZengWileyInternational Journal of Aerospace Engineering1687-59742023-01-01202310.1155/2023/3546838Aviation Rivet Classification and Anomaly Detection Based on Deep LearningXiao-bo Zhu0School of Air Traffic ManagementThe shortage of personnel and the high cost have become a major pain point in the current safety supervision work of the inspectors. Aiming at the problem that the aircraft maintenance inspector could not visit the scene in person during the epidemic, a remote safety supervision platform was built based on intelligent glasses and 5G network, and the real-time monitoring of the aircraft skin rivet status was realized. And a method of aviation rivet classification and anomaly detection based on deep learning algorithm was proposed. Firstly, according to the appearance of rivet head, the aviation rivet is classified, the data set of aviation rivet is made, and the aviation rivet classification and anomaly detection model are constructed. Evaluate the detection results from such indicators as confidence, precision, recall rate, and mAP and compare the algorithm with the detection results of Yolox-s, Yolox-m, Yolov5-s, Yolov5-m, and Yolov4. The results show that (1) the algorithm proposed in this paper can realize the classification of aviation rivets and the detection of abnormal conditions, the confidence of the detection results is more than 90%, and the average precision, recall, and AP value are above 95%, 85%, and 88%, respectively. (2) The order of rivet classification and abnormal detection effect from good to bad is Philips screws, round head rivets, flat head rivets, countersunk head rivets, blind rivets, and abnormal condition. (3) Compared with other algorithms, the aviation rivet classification abnormal target detection based on deep learning has absolute advantages in accuracy and speed.http://dx.doi.org/10.1155/2023/3546838 |
| spellingShingle | Xiao-bo Zhu Aviation Rivet Classification and Anomaly Detection Based on Deep Learning International Journal of Aerospace Engineering |
| title | Aviation Rivet Classification and Anomaly Detection Based on Deep Learning |
| title_full | Aviation Rivet Classification and Anomaly Detection Based on Deep Learning |
| title_fullStr | Aviation Rivet Classification and Anomaly Detection Based on Deep Learning |
| title_full_unstemmed | Aviation Rivet Classification and Anomaly Detection Based on Deep Learning |
| title_short | Aviation Rivet Classification and Anomaly Detection Based on Deep Learning |
| title_sort | aviation rivet classification and anomaly detection based on deep learning |
| url | http://dx.doi.org/10.1155/2023/3546838 |
| work_keys_str_mv | AT xiaobozhu aviationrivetclassificationandanomalydetectionbasedondeeplearning |