针对汽车疲劳驾驶导致交通事故频发的问题,设计实现了一种基于人脸特征识别的汽车疲劳驾驶预警系统。该系统首先通过摄像头采集驾驶员面部图像数据,再利用深度学习中的卷积神经网络进行人脸特征提取,获取包含眨眼频率、打哈欠次数、头部姿态等关键特征参数。在此基础上,建立了多维度的疲劳评估模型,该模型通过分析连续视频帧中面部特征的动态变化规律,实现了对驾驶疲劳状态的实时监测和预警。为提高系统性能,对传统卷积神经网络结构进行了优化,增加了注意力机制模块,同时采用了长短时记忆网络(LSTM)来捕捉驾驶员面部特征的时序变化特性。实验结果表明,该系统在不同光照和驾驶环境下的疲劳检测准确率达到95.3%,平均响应时间低于0.5秒,具有较强的实用性和可靠性,能够有效降低疲劳驾驶带来的安全风险。In response to the problem of frequent traffic accidents caused by car fatigue driving, a car fatigue driving warning system based on facial feature recognition has been designed and implemented. The system first collects facial image data of the driver through a camera, and then uses convolutional neural networks in deep learning to extract facial features, including key feature parameters such as blink frequency, yawning frequency, and head posture. On this basis, a multidimensional fatigue assessment model was established, which achieved real-time monitoring and warning of driving fatigue status by analyzing the dynamic changes of facial features in continuous video frames. To improve system performance, the traditional convolutional neural network structure was optimized by adding an attention mechanism module, and a long short-term memory network (LSTM) was used to capture the temporal variation characteristics of driver facial features. The experimental results show that the fatigue detection accuracy of the system reaches 95.3% under different lighting and driving environments, with an average response time of less than 0.5 seconds. It has strong practicality and reliability, and can effectively reduce the safety risks caused by fatigue driving.
动态运动原语(Dynamic Movement Primitives,DMP)算法是工业机械手中常见的模仿学习算法,当前DMP算法发展迅速,但是在能源消耗方面优化较少。针对国家绿色环保战略和企业用人用工成本等问题,提出了一种以减少能源消耗和机械手示教快速...
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动态运动原语(Dynamic Movement Primitives,DMP)算法是工业机械手中常见的模仿学习算法,当前DMP算法发展迅速,但是在能源消耗方面优化较少。针对国家绿色环保战略和企业用人用工成本等问题,提出了一种以减少能源消耗和机械手示教快速复现为目的改进DMP的机械手模仿学习算法。该算法在DMP的基础上优化了示教路径并增加了对能源消耗的控制,首先对机械手拖动示教后的轨迹编码进行优化,在示教轨迹中添加机械手抓取的关键点,通过关键点优化DMP的示教编码去除多余动作并计算其中能源消耗最小的机械手电机运行策略和最优路径。最后通过机械手进行复现操作,在PyBullet仿真平台中搭建了一个7轴冗余机械手和若干抓取物并设定相应的物理规则来实现算法。经目标抓取实验测试:基于改进DMP的机械手模仿学习算法与传统DMP模仿学习算法比较,在保证任务成功率的基础上综合降低了42.82%能源的消耗,验证了该方法的有效性。
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