Abstract: With the popularization of the concept of smart city,the intelligent management of traffic road has become the focus of scholars.In order to solve the problem of road traffic statistics,this paper proposes a residual network based UAV aerial traffic flow measuring algorithm based on residual network.The fully connected multi-scale residual learning block (FMRB) is introduced into the method network to solve the gradient dispersion phenomenon and make the image features better extracted and learned.At present,the accuracy of the existing vehicle detection algorithms is low,and most of them can only detect the vehicle,and can not count the traffic flow.In this paper,combined with video frame estimation method,real-time monitoring and statistics of traffic flow is realized.Compared with SSD,YOLOv2 and YOLOv3 algorithms in vehicle detection performance,the results show that,under the condition of self built data set training,this method introduces multi-scale residual learning block (FMRB) for vehicle recognition of remote sensing image,and can achieve higher recognition accuracy.In the field traffic flow monitoring,the error detection rate is less than 1%,which has strong practical effect.

Key words: Deep learning, Intelligent transportation, Residual network, Traffic flow measuring, Vehicle recongnition