A Novel Neural Network Model Based on Real Mountain Road Data for Driver Fatigue Detection.

Dabing Peng, Junfeng Cai, Lu Zheng, Minghong Li, Ling Nie, Zuojin Li

Abstract read

In one paragraph

Article in Biomimetics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Dabing PengSchool of Electronic and Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.

Junfeng CaiSchool of Electronic and Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.

Lu ZhengSchool of Electronic and Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.

Minghong LiSchool of Electronic and Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.

Ling NieSchool of Electronic and Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.

Zuojin LiSchool of Electronic and Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.ORCID 0000-0002-8154-3968

Funding

National Natural Science Foundation of China 61873043

6 · The paper itself

Abstract

Mountainous roads are severely affected by environmental factors such as insufficient lighting and shadows from tree branches, which complicates the detection of drivers' facial features and the determination of fatigue states. An improved method for recognizing driver fatigue states on mountainous roads using the YOLOv5 neural network is proposed. Initially, modules from Deformable Convolutional Networks (DCNs) are integrated into the feature extraction stage of the YOLOv5 framework to improve the model's flexibility in recognizing facial characteristics and handling postural changes. Subsequently, a Triplet Attention (TA) mechanism is embedded within the YOLOv5 network to bolster image noise suppression and improve the network's robustness in recognition. Finally, the Wing loss function is introduced into the YOLOv5 model to heighten the sensitivity to micro-features and enhance the network's capability to capture details. Experimental results demonstrate that the modified YOLOv5 neural network achieves an average accuracy rate of 85% in recognizing driver fatigue states.

Indexed as

fatigue driving detectionfeature extractionTA mechanismYOLOv5

Identifiers

PMID39997127

DOI10.3390/biomimetics10020104

PMCPMC11853368

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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.