In this paper, we propose a pilot hand detection method that combines the semi-supervised DenSe Learning (DSL) with a coordinate attention (CA) module, which we call CA-DSL. In our suggested approach, the CA module is added to Resnet, the backbone network of DSL. The CA module enhances the DSL’s capacity for learning and boosts the effectiveness of pilot hand detection. In addition, we establish a dataset for pilot hand detection that we call Pilot Hand (PH). On the PH dataset, we evaluate the suggested hand detection algorithm. and compared it with the state-of-the-art models. Experimental results show that the detection accuracy of CA-DSL outperforms the state-of-the-art models by 2%-7%.
Pilot Hand Detection Based on Semi-Supervised Learning
12.10.2022
1975480 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Driver Distraction Detection Using Semi-Supervised Machine Learning
Online Contents | 2016
|Driver Distraction Detection Using Semi-Supervised Machine Learning
Online Contents | 2015
|Object Detection in Aerial Imagery Based on Enhanced Semi-Supervised Learning
British Library Conference Proceedings | 2005
|