The invention provides a railway artificial intelligence flaw judgment method based on image recognition. The method comprises the following steps of: building a wheel-roller coupling theoretical model through the combination position relation and sound propagation characteristics of a detection wheel and a steel rail; performing a simulation test by using the wheel-roller coupling theoretical model, and transmitting a simulation result to a cloud server; and enabling a detection vehicle to transmit a daily monitoring record to the cloud server, and enabling the cloud server to compare the daily monitoring record with the simulation result, extract damage characteristics in the simulation result, and establish a damage characteristic library; and judging the damage of the characteristics in the damage characteristic library through an image recognition technology. According to the artificial intelligence damage judgment method, abstract information is obtained from big data by adopting the effective artificial intelligence technology, and a series of nonlinear transformations are adopted by utilizing a machine deep learning technology and a data driving mode, and characteristic information of various track defects is extracted from a low layer to a high layer and from a concrete aspect to an abstract aspect from the original data of the defects, and therefore, railway artificial intelligence damage judgment based on image recognition is realized.
本发明提供了一种基于图像识别的铁路人工智能判伤方法,包括以下:利用探轮与钢轨间结合位置关系和声传播特性,建立轮辊耦合理论模型;利用轮辊耦合理论模型进行仿真测试,并将仿真结果传送给云端服务器;检测车将日常监测记录传送给云端服务器,并与仿真结果进行特征对比,提取其中的损伤特征,并建立损伤特征库;通过图像识别技术对损伤特征库内的特征进行判伤。本发明所述的人工智能判伤方法采用有效的人工智能技术从大数据中获取抽象信息,利用机器深度学习技术,通过数据驱动的方式,采用一系列的非线性变换,从缺陷的原始数据中提取由低层到高层、由具体到抽象的各种轨道缺陷的特征信息,从而实现图像识别的铁路人工智能判伤。
Railway artificial intelligence damage judging method based on image recognition
一种基于图像识别的铁路人工智能判伤方法
2021-04-13
Patent
Electronic Resource
Chinese
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