ArticleScientific reports2025
An intelligent network framework for driver distraction monitoring based on RES-SE-CNN.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Real-Time Fatigue Monitoring Using sEMG and HRV Sensors for Industrial Operators Under Swing Conditions.Sensors (Basel, Switzerland) · 2026Article
- LiteDriveNet for driver distraction classification using a lightweight multi-scale convolutional neural network.Scientific reports · 2026Article
- Entropy-Driven Intelligent Diagnosis for SMR Loss of Coolant Accidents: A CNN-LSTM-Attention Hybrid Model for Break Size Assessment.Entropy (Basel, Switzerland) · 2026Article
- A hybrid BiLSTM-XGBoost model for short-term forecasting of key parameters in nuclear power systems.Scientific reports · 2026Article
- Multiscale wavelet attention convolutional network for facial expression recognition.Scientific reports · 2025Article
- NeuroSafeDrive: An Intelligent System Using fNIRS for Driver Distraction Recognition.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
As the quantity of motor vehicles and drivers experiences a continuous upsurge, the road driving environment has grown progressively more complex. This complexity has led to a concomitant increase in the probability of traffic accidents. Ample research has demonstrated that distracted driving constitutes a primary human - related factor precipitating these accidents. Therefore, the real - time monitoring and issuance of warnings regarding distracted driving behaviors are of paramount significance. In this research, an intelligent driver state monitoring methodology founded on the RES - SE - CNN model architecture is proposed. When compared with three classical models, namely VGG19, DenseNet121, and ResNet50, the experimental outcomes indicate that the RES - SE - CNN model exhibits remarkable performance in the detection of driver distraction. Specifically, it attains a correct recognition rate of 97.28%. The RES - SE - CNN network architecture model is characterized by lower memory occupancy, rendering it more amenable to deployment on vehicle mobile terminals. This study validates the potential application of the intelligent driver distraction monitoring model, which is based on transfer learning, within the actual driving environment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.