In view of the problems existing in tactile pavement,a feasible tactile paving accessible breakpoint recognition method is proposed through the application of classification algorithm,and based on user behavior and tactile paving recognition characteristics.Firstly,the user's behavior is simulated,and the tactile pavement breakpoint classification is realized based on Graph Neural Network(GNN).Combined with algorithm of object detection,an intelligent recognition model is constructed.Secondly,the object type is clearly identified and the data set is constructed by combining the general codes for accessibility and statistical methods.And the model is optimized according to the supervised machine learning results.Finally,the feasibility and advantages of this method are demonstrated through practical cases,and the application prospect of this method is described in combination with the concept of"Urban Brain"and"Crowdsourcing".The experimental results show that this method has higher accuracy than other classification algorithms,and can realize efficient,accurate,systematic and automatic recognition of tactile pavement problems by machine instead of manual.
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