Automatic marking of semi supervised landing gears based on CenterNet

DOI:	10.11809/bqzbgcxb2023.04.034
Keywords:	automatic image annotation; CenterNet; channel attention mechanism; semi supervised learning; target detection model
Abstract:	Aiming at the time consuming and laborious problem of manual tagging of aircraft landing gears, this paper proposes an automatic tagging of aircraft landing gears by combining the target detection model of CenterNet with semi supervised learning. Based on ResNet50, the backbone feature network of CenterNet, this method embeds the channel attention mechanism and verifies its effectiveness. Then, combined with semi supervised learning, unlabeled samples are marked with the model of labeled sample training, and the obtained problem samples are manually corrected and superimposed into the original labeled samples to form a new dataset for further training. Finally, a target detection model with good performance and automatic labeling is generated. The experimental results show that, after five iterative training for the model, the precision of the annotation model is 95.29%, and the average accuracy is 92.16%, which meets the annotation requirements for the positioning of the aircraft landing gear.
Issue:	Vol. 44 No. 4 (2023)
Published:	2023-04-28
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