A method to detect traffic dangers based on visual attention model of sparse sampling was proposed. The hemispherical sparse sampling model was used to decrease the amount of calculation which increases the detection ...A method to detect traffic dangers based on visual attention model of sparse sampling was proposed. The hemispherical sparse sampling model was used to decrease the amount of calculation which increases the detection speed. Bayesian probability model and Gaussian kernel function were applied to calculate the saliency of traffic videos. The method of multiscale saliency was used and the final saliency was the average of all scales, which increased the detection rates extraordinarily. The detection results of several typical traffic dangers show that the proposed method has higher detection rates and speed, which meets the requirement of real-time detection of traffic dangers.展开更多
重大装备制造中厚板机器人多层多道焊(multi-layer and multi-pass welding,MLMPW)一直是热点和难点,而实现机器人MLMPW的核心是对其熔池的获取、监控并分类.为了提高MLMPW的自动化和智能化,有必要开发一个熔池图像在线分类系统.针对焊...重大装备制造中厚板机器人多层多道焊(multi-layer and multi-pass welding,MLMPW)一直是热点和难点,而实现机器人MLMPW的核心是对其熔池的获取、监控并分类.为了提高MLMPW的自动化和智能化,有必要开发一个熔池图像在线分类系统.针对焊接过程中的熔池图像提出了一种新的MLMPW熔池分类方法——基于视觉注意的(SENet)VGGNet熔池分类方法.为了提高效率和精度,引入迁移学习中的预训练模型到网络训练过程中.因为针对中厚板多层多道熔池研究较少,导致熔池公开数据集较少,为了应对这一问题,需要对数据集进行增广.结果表明,提出的模型可快速有效的对七类MLMPW熔池进行准确分类,预测精度可达到98.39%.展开更多
基金Project(50808025)supported by the National Natural Science Foundation of ChinaProject(20090162110057)supported by the Doctoral Fund of Ministry of Education of China
文摘A method to detect traffic dangers based on visual attention model of sparse sampling was proposed. The hemispherical sparse sampling model was used to decrease the amount of calculation which increases the detection speed. Bayesian probability model and Gaussian kernel function were applied to calculate the saliency of traffic videos. The method of multiscale saliency was used and the final saliency was the average of all scales, which increased the detection rates extraordinarily. The detection results of several typical traffic dangers show that the proposed method has higher detection rates and speed, which meets the requirement of real-time detection of traffic dangers.
基金This work was supported in part by the Foundation of Guangdong Educational Committee （2014KTSCX191） and the National Natural Science Foundation of China （61201087）.
文摘重大装备制造中厚板机器人多层多道焊(multi-layer and multi-pass welding,MLMPW)一直是热点和难点,而实现机器人MLMPW的核心是对其熔池的获取、监控并分类.为了提高MLMPW的自动化和智能化,有必要开发一个熔池图像在线分类系统.针对焊接过程中的熔池图像提出了一种新的MLMPW熔池分类方法——基于视觉注意的(SENet)VGGNet熔池分类方法.为了提高效率和精度,引入迁移学习中的预训练模型到网络训练过程中.因为针对中厚板多层多道熔池研究较少,导致熔池公开数据集较少,为了应对这一问题,需要对数据集进行增广.结果表明,提出的模型可快速有效的对七类MLMPW熔池进行准确分类,预测精度可达到98.39%.
