拖拉机驾驶员面部遮挡检测与疲劳判别的方法与系统
By constructing a facial occlusion detection and fatigue discrimination model using deep learning technology, the accuracy problems of facial occlusion and fatigue detection for tractor drivers were solved, enabling real-time and accurate discrimination of the fatigue state of tractor drivers and ensuring driving safety.
CN121033905BActive Publication Date: 2026-07-17HUAZHONG AGRI UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUAZHONG AGRI UNIV
- Filing Date
- 2025-07-02
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing technologies cannot effectively detect facial occlusion and fatigue in tractor drivers, resulting in low detection accuracy and making them unsuitable for complex agricultural operating environments.
Method used
Deep learning technology is used to construct facial occlusion detection model, occlusion repair model, facial key point detection model and fatigue detection model. Through image acquisition, processing and analysis, the detection and discrimination of driver facial occlusion and fatigue can be realized.
Benefits of technology
It improves the accuracy of facial key point detection, enables effective identification of tractor driver fatigue, ensures driving safety, and meets the requirements of real-time performance and accuracy.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN121033905B_ABST
Abstract
本发明涉及一种拖拉机驾驶员面部遮挡检测与疲劳判别的方法与系统，属于驾驶安全检测领域，包括：采集驾驶员面部图像并标注面部特征以构建数据集；基于该数据集采用预设训练配置构建面部遮挡检测模型，提取被遮挡的面部特征；利用预设不规则掩膜数据集构建面部遮挡修复模型，修复被遮挡的面部特征；基于修复后的特征和预设面部关键点数据集，构建面部关键点检测模型以检测面部关键点；最后将检测到的面部关键点输入预配置的面部疲劳检测模型，检测驾驶员的面部疲劳特征并判断其疲劳状态。本发明通过对遮挡面部特征的检测与修复，提高了后续面部关键点检测的准确性，从而实现对拖拉机驾驶员疲劳状态的有效判别，为保障驾驶安全提供了技术支持。
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