Intelligent water level measurement algorithm for urban rivers based on improved YOLOX
In response to the problem of insufficient feature information extraction in current deep learning based water level measurement algorithms,an intelligent water level measurement algorithm for urban rivers based on improved YOLOX is proposed.To improve the recognition rate of YOLOX for multi-class dense targets,CBAM attention mechanism is introduced in the feature fusion network,and a loss function D-IoU based on calculating target box information is adopted to accelerate the convergence of the model.This algorithm uses the improved YOLOX to identify and statistically analyze the scales and numbers on the water gauge,and calculate the water level value.The experiment shows that the proposed method has an av-erage recognition rate of 98.62%and 92.23%for water level scale and number,respectively.The final average error in cal-culating water level is 1.16cm,which is 1.76cm less than the average error of other image recognition water level measure-ment algorithms.It can achieve high-precision intelligent measurement of water level values in urban rivers.
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