.png){kind=icon}
返回
Facial Feature Extraction Algorithm Based on Improved YOLOv7-Tiny
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2025.3534235.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Facial feature extraction is a critical step in driver fatigue detection, essential for improving driving safety. This paper proposes a novel driver facial feature extraction algorithm based on YOLOv7-Tiny to address the challenges of low precision and practical deployment in existing fatigue detection systems. The algorithm employs depthwise-separable convolution combined with spatial depth convolution to effectively extract key facial features. An attention module is integrated between the backbone and neck networks to enhance contextual understanding and filter out irrelevant information, enabling precise extraction of detailed and distinguishable features, even under varying lighting conditions and driver poses. Additionally, a feature fusion module is introduced to merge features from different receptive fields, improving multi-scale feature extraction and reducing the miss rate for small objects. The proposed algorithm achieves a detection accuracy of 59.8% mAP on the Drowsy-Driving-Det dataset, marking an 8.5% improvement over the original method, alongside a 60.9% reduction in model parameters. This improved algorithm not only meets real-time deployment requirements but also maintains high detection accuracy, making it well-suited for facial fatigue feature extraction in complex driving environments and edge deployment scenarios.
Keyword:
Fatigue
Feature extraction
Vehicles
Accuracy
Biomedical monitoring
Real-time systems
Monitoring
Facial features
Accidents
Trajectory
Deep learning
facial feature extraction
feature fusion
small object detection
YOLO
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
暂无机构信息
引用论文
Overexpression of NAF-1 ameliorates Ischemic Stroke in rats by inhibiting PINK1/Parkin-mediated mitophagyNAF-1过表达通过抑制PINK1/Parkin介导的线粒体自噬改善大鼠缺血性脑卒中.png)
IF0
PREAI
分享收藏
查看更多内容