@article{ref1,
title="A self-learning mechanism-based approach to helicopter entry and departure recognition",
journal="Sensors (Basel)",
year="2022",
author="Lyu, Zonglei and Chang, Xuepeng and An, Wei and Yu, Tong",
volume="22",
number="20",
pages="e7852-e7852",
abstract="In order to accurately record the entry and departure times of helicopters and reduce the incidence of general aviation accidents, this paper proposes a helicopter entry and departure recognition method based on a self-learning mechanism, which is supplemented by a lightweight object detection module and an image classification module. The original image data obtained from the lightweight object detection module are used to construct an Automatic Selector of Data (Auto-SD) and an Adjustment Evaluator of Data Bias (Ad-EDB), whereby Auto-SD automatically generates a pseudo-clustering of the original image data. Ad-EDB then performs the adjustment evaluation and selects the best matching module for image classification. The self-learning mechanism constructed in this paper is applied to the helicopter entry and departure recognition scenario, and the ResNet18 residual network is selected for state classification. As regards the self-built helicopter entry and departure data set, the accuracy reaches 97.83%, which is 6.51% better than the bounding box detection method. To a certain extent, the strong reliance on manual annotation for helicopter entry and departure status classification scenarios is lifted, and the data auto-selector is continuously optimized using the preorder classification results to establish a circular learning loop in the algorithm.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22207852",
url="http://dx.doi.org/10.3390/s22207852"
}