随着新交通法的实施,电瓶车骑手的头盔佩戴问题引起社会关注,尽管通过使用摄像机可以对骑手进行间接观测,从而减少了直接观察所带来的人力成本,但骑手位置的不确定性,不安全行为的随机性,都给通过固定式摄像机的骑手头盔佩戴检测的准确性和通用性带来了极大挑战.为此,本文设计了一种基于无人机巡检和深度学习的电瓶车骑手佩戴检测方法,该方法首先基于YOLO模型针对无人机采集图片进行分析处理,获取骑手头盔佩戴特征,通过特征分析建立未佩戴头盔骑手的分类模型;然后,针对实际采集过程中的噪声影响,对算法的性能进行验证,并通过噪声抑制方法进一步提升了识别的准确性.所提出的方法可以为电瓶车骑手的安全行驶检测提供新的技术手段,有利于进一步提升交管部门的管理效率.

为测量与分析运动员运动过程中动态足底压力变化,优化训练模式与避免其运动损伤,该文研制了一种新型可穿戴式动态足底压力测量系统.以压电驻极体薄膜(EMFi)为传感材料,采集足底16个点的压力信号,原始电荷信号经过高速模拟开关、电荷放大电路、低通滤波放大电路、工频滤波电路、STM32F4内部A/D采集,并由2.4 GHz无线发送至上位机.实验结果显示,该系统测试结果与人体正常运动步态变化趋势一致,且具有良好的动态响应和可靠性,为运动员的姿态矫正提供了理论依据.

针对基于传统特征提取方法的远红外图像行人检测存在准确率和实时性不足的问题,本文研究了一种基于改进YOLO模型的远红外行人检测方法,通过改进其深度卷积神经网络的输入分辨率,然后在基于实际道路采集的红外数据集上进行训练,得到检测效果最佳的检测模型,并提出基于车速的自适应图像分辨率模型,以提高车载系统的行人检测性能.在基于实际道路的红外数据集上的对比实验表明,该方法与传统方法相比,准确率从76.5％提高到89.2％,每秒传输帧数从0.01259 f/s提高到40.5 f/s,满足车载情况下的实时性需求.

To solve the problems of low recognition accuracy and slow convergence speed for driving behavior in driving assistant technology,a new driving behavior recognition method was proposed based on improved LRCN model. The self driving behavior data sets were adopted as input samples and were processed by Pyramid down sampling and Gauss mixture model feature extraction preprocessing algorithm, and the standard video image sequence was obtained. The image sequence was introduced into the model based on convolutional neural network and gating unit recursive network,and the optimization was conducted to get the final result of convergence. The model was calculated on the GPU with Keras framework,and environment adaptability,preprocessing algorithm and model comparison experiments were carried out respectively. The results show that the pretreatment algorithm can guarantee the convergence of the proposed model and can improve the robustness of the model recognition in different scenes and different test objects. The average recognition accuracy in the self building data set reaches 94. 3% and is 4.7% higher than that of the traditional LRCN model. The model also has faster convergent speed and stronger generalization ability.