ICMFed: An Incremental and Cost-Efficient Mechanism of Federated Meta-Learning for Driver Distraction Detection

Guo, Zihan; You, Linlin; Liu, Sheng; He, Junshu; Zuo, Bing-Ran

doi:10.3390/math11081867

Cited by 4 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the strong feature learning ability and excellent generalization ability, deep neural networks can overcome the shortcomings of traditional machine learning methods and have become more and more popular [16,23,24]. Convolutional neural networks excel at learning high-level abstract features from images, so researchers have proposed a large number of driver distraction detection methods based on convolutional neural networks.…”

Section: Driver Distraction Detection Methodsmentioning

confidence: 99%

Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer

Zhang,

Yang,

2024

Vehicles

View full text Add to dashboard Cite

Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on.

show abstract

Section: Driver Distraction Detection Methodsmentioning

confidence: 99%